Reviewerly/exHarmony huggingface dataset:
The Realization of Electronic Platform for the Professional Courses of Power Electronics and Power Transmission in Colleges Under the Background of Internet +

Based on the Internet+background, this paper studies the electronic platform of power electronics and power transmission professional courses, and discusses the course teaching content and teaching methods, aiming to cultivate high-quality technical talents with solid theoretical foundation knowledge and practical skills. It also proposes a comprehensive experimental program for undergraduate teaching of new energy power generation, flexible direct power transmission and motor frequency conversion speed regulation. Combining the current social employment and talent needs, using J2EE, MySQL and web technologies, the platform courses are optimized, integrated, and updated. The concept of talent training will further deepen the reform and innovation of teaching methods and means.
Information Literacy Skills and the Utilization of Information Resources by Public Health Workers in Federal Neuro-Psychiatric Hospital Calabar

This study investigated the influence of “information literacy skills on the utilization of information resources by public health workers in Federal Neuro-Psychiatric Hospital Calabar”. Three research hypotheses were formulated to guide the study. Survey research design was adopted for the study. Purposive sampling technique was utilized to select subjects of the study. Ninety five out of the one hundred and twenty five copies of the questionnaire administered were retrieved and found treatable. A well-structured and validated questionnaire named information literacy skill and utilization of information resources (ILSUIR) questionnaire was used to collect information from respondents. Cronbach’s Alpha reliability method was used to determine the reliability of research instrument, and correlation coefficient range of 0.74 to 0.89 was arrived at. These were considered high enough to justify the conclusion that the instrument is reliable in measuring the variables set for the study. Data obtained were subjected to statistical analyses using Independent t-test analysis. The results revealed that access point, ICT competence and information evaluation skills significantly influenced utilization of information resources by public health workers of Federal Neuro-Psychiatric Hospital Calabar”. Based on the findings it was recommended that library literacy skill programs should be organized by the authorities in the study area in order to update the staff and other users of the library on access point. It was equally recommended that the use of information communication technology (ICT) should be encouraged while evaluation of information resource toward effective library utilization should be carried out periodically.
Distributed Classification Model of Streaming Tweets based on Dynamic Model Update

In this study, we propose a distributed architecture that dynamically updates the model for classifying tweet streams generated in real time. Our architecture ingests data streams through Apache Kafka and classifies them based on Apache Spark Streaming. In order to dynamically reflect input stream changes into the classification model, we design the classification model that can be dynamically updated by updating the tokenizer and classifier for new tweet streams. The proposed architecture can provide effective classification for data streams due to the dynamic update and can efficiently process through parallel processing of distributed environments. Through experiments using cyberattack-related tweets, we show that our classification model gradually improves the classification accuracy from 0.8869 when the initial 50,000 tweets are used to 0.9094 when 200,000 tweets are accumulated by F1-score.
Measurement of visibility and phase steps of a static wind imaging interferometer assisted by deep learning

In a static wind imaging Michelson interferometer we developed, one of the Michelson mirrors is divided into four quadrants, with coatings on the quadrants that provide small phase steps from one quadrant to another, realizing the four simultaneous sampling of the interferogram. Restricted by the coating process and interferometer adjustment, the instrument visibility and phase steps of the four quadrants will deviate from the design value. In the actual passive detection of the atmospheric wind field, quasi-real-time calibration is required, and the calibration will also be affected by the instrument noise. In this paper, we propose a deep-learning-based denoising algorithm that can quickly denoise the wind interferogram with no need to adjust parameters, combined with conventional least-squares fitting cosine curves to obtain the visibility and phase steps of four quadrants from a series of interferograms with varying phase differences. The proposed algorithm framework is verified by experiment, and the measurement of visibility and phase steps of the wind field interferogram is efficiently realized. It can provide a reference for the visibility and phase steps measurement of the wind imaging interferometer and may have applications in wind imaging interferometer calibration.
THE FEATURES OF ATTITUDES TO DIGITAL EDUCATIONAL TECHNOLOGIES IN RUSSIAN UNIVERSITY TEACHERS OF DIFFERENT DISCIPLINES

I. Novikova , P. BychkovaPeoples’ Friendship University of Russia (RUDN University) (RUSSIAN FEDERATION)
Adversarial Jamming for a More Effective Constellation Attack

The common jamming mode in wireless communication is band barrage jamming, which is controllable and difficult to resist. Although this method is simple to implement, it is obviously not the best jamming waveform. Therefore, based on the idea of adversarial examples, we propose the adversarial jamming waveform, which can independently optimize and find the best jamming waveform. We attack QAM with adversarial jamming and find that the optimal jamming waveform is equivalent to the amplitude and phase between the nearest constellation points. Furthermore, by verifying the jamming performance on a hardware platform, it is shown that our method significantly improves the bit error rate compared to other methods.
Fano effect induced giant and robust enhancement of photon correlations in cavity QED systems

The Fano effect arising from the interference between two dissipation channels of the radiation continuum enables tuning of the photon statistics. Understanding the role of the Fano effect and exploiting it to achieve strong photon correlations are of both fundamental and applied significance. We present an analytical description of Fano-enhanced photon correlations based on cavity quantum electrodynamics to show that the Fano effect in atom-cavity systems can improve the degree of antibunching by over four orders of magnitude. The enhancement factors and the optimal conditions are explicitly given, and found to relate to the Fano parameter q. Remarkably, the Fano enhancement manifests robustness against the decoherence processes and can survive in the weak coupling regime. We expect our work to provide insight to tuning the photon statistics through the Fano effect, which offers a new, to the best of our knowledge, route to enhance the photon correlations, as well as the possibility of generating nonclassical light in a wider diversity of systems without the need of a strong light-matter interaction.
Comparative Study of Multiple Fitting Regression and Bayes and Probabilistic Support Vector Machine Methods in Classification of Single-cell RNA Data

With the development of single-cell RNA sequencing technology, it is very important and valuable to supplement and improve the mining algorithm of single-cell RNA data to understand the heterogeneity of single-cell RNA and the precise mechanism of the prevention and treatment of diseases.Machine learning and data mining are the preferred technologies for processing large amounts of data.The multiple fitting regression and Bayes (MFRB) method is a new method that combines multiple fitting regression (MFR) methods and Bayesian decision-making in machine learning.The probabilistic support vector machine (PSVM) method is excellent for data classification and has been widely used and verified.In this study, these two classification methods were used to detect large-scale single-cell RNA data and small-sample unbalanced single-cell RNA data, respectively.The performances of the two algorithms were determined and their classification effects were discussed.A random walking preprocessing algorithm is also used to improve the distribution characteristics of low-quality data.The results show that the two algorithms have good results only for large-scale single-cell RNA data; for small-sample unbalanced data sets, neither of the algorithms effectively classified single-cell RNA data.
Student Lean-Clubs: Source of Continuous Improvement оf the University Performance

Educational organizations require a fundamentally new organization of business processes. The transition to blended and distance learning (remote work) is the first reason. The second reason is the problems of Russian higher education that have accumulated earlier. Universities need to improve the efficiency of personnel, eliminate losses, maintain and improve the quality of the educational product. Lean production tools and techniques are suitable for these tasks. Lean production approaches have been used in large industrial plants around the world for many years. The expansion of lean practices to the service and public sector is a current trend. The ideology of lean production is also permeating higher education institutions. The Lean Universities Association contributes to this process. The National Project “Labor Productivity” is also a driver of development. Universities are training personnel for the implementation of this project. The students’ lean-club can be a successful form of interaction with students. Lean-club members will participate in the analysis of processes and generation of ideas for continuous improvement of the university. The activities of the lean-club will improve the work of the university and form a lean thinking among its members. The Kaizen quality philosophy can form the basis of the lean-club’s concept. It involves the gradual but constant implementation of improvements. The main tool will be quality control circles. Their participants will analyze business processes and make proposals for their improvement. Both Kaizen philosophy and lean production are customer focused. Students are customers and consumers of most of the key processes of the university. Building a system of continuous improvement of the university with the participation of students is considered appropriate. Student lean-club members need certain personal and professional competencies to implement projects. The most important competencies are soft skills and basic knowledge of lean production. They do not prevent you from joining the lean-club. Lean-club members can acquire these competencies as they work. Learning in practice, in the process of project activities from simple to complex, seems to be important and valuable.
Internet of Things (IoT)

The rise in ubiquitous and embedded computing devices have led to the concept of Internet of Things (IoT) that are now used in various applications such as smart homes, smart cities, and precision agriculture, to name a few. While a good deal of research is being done on the design, development, and implementation of IoTs and IoT-enabled systems, few have been directed at the associated ethical issues and privacy concerns of IoT that affect individuals and society. This chapter discusses the impact of IoTs on individuals and society and further explores cases, where IoTs monitoring individuals in various capacities impact their daily lives – e.g., vehicle usage, individual company, finances and business, user preferences for technology, state-of-health and well-being, behavior in the open environment. Discussion on the societal issues of monitoring agricultural production, availability of beds and doctors in hospitals, user buying patterns, etc. are also considered in this chapter. This chapter also discusses the ethical aspects and privacy concerns of individuals and society, when and where IoT usage becomes widespread. Several open research issues to address the ethical and privacy concerns of IoT enablement and their panopticon effects are also discussed, making a call for strong legislative measures on privacy.
The Design of Medical English Autonomous Guiding Platform under the Information Technology Environment-Based on R Language

This paper designs the design of a medical English autonomous learning platform based on R language in an information technology environment. R language is a data processing method based on statistics, calculations and graphics. The medical English autonomous learning model includes autonomous learning classroom teaching mode and autonomous learning extracurricular Mutual assistance mode, which can ideally carry out big data processing work. When the user request layer of the platform sends big data processing instructions to the technical layer, the technical layer immediately starts to work, and its processing results will also be fed back to the user request layer. By analyzing the main problems of autonomous learning of college English under the network environment, it puts forward corresponding strategies on how to improve the autonomous learning model of college English under the network environment.
Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteristics of business English, the improved GLR algorithm is used for collection, sorting, and analysis, so as to realize the intelligent recognition of business English. The results show that based on the improved GLR algorithm, the recognition accuracy is high, and the comprehensive score is 92.5 points, which overcomes the disadvantages of the GLR algorithm, and the operation speed and processing are improved. Based on the improved GLR algorithm, the intelligent translation of business English is realized, which is accurate and fast, and greatly promotes the learning and development of business English.
Speaker-Aware Mixture of Mixtures Training for Weakly Supervised Speaker Extraction

Dominant researches adopt supervised training for speaker extraction, while the scarcity of ideally clean corpus and channel mismatch problem are rarely considered. To this end, we propose speaker-aware mixture of mixtures training (SAMoM), utilizing the consistency of speaker identity among target source, enrollment utterance and target estimate to weakly supervise the training of a deep speaker extractor. In SAMoM, the input is constructed by mixing up different speaker-aware mixtures (SAMs), each contains multiple speakers with their identities known and enrollment utterances available. Informed by enrollment utterances, target speech is extracted from the input one by one, such that the estimated targets can approximate the original SAMs after a remix in accordance with the identity consistency. Moreover, using SAMoM in a semi-supervised setting with a certain amount of clean sources enables application in noisy scenarios. Extensive experiments on Libri2Mix show that the proposed method achieves promising results without access to any clean sources (11.06dB SI-SDRi). With a domain adaptation, our approach even outperformed supervised framework in a cross-domain evaluation on AISHELL-1.
Perspective Chapter: Evolution of User Interface and User Experience in Mobile Augmented and Virtual Reality Applications

An end-use’s experience of any software is typically influenced by the interface presented by the application to the user. For Mixed Reality Environments such as Augmented and Virtual Reality, the user interface is highly visual, and a poor interface can significantly degrade the user experience. Adequate attention is required when designing or creating interfaces and user experience within Mixed Reality Environments as traditional interface design goals and specifications often need to be adjusted. Furthermore, for mixed reality environments on Mobile devices there are additional interface constraints and considerations that would considerably improve the user experience when properly addressed. This research paper discusses the evolution(s) of user interface(s) and user experience of Augmented and Virtual Reality applications on Mobile devices and contributes a framework for improving user interfaces and experience when using Mixed Reality Environments.
Designing Teachers’s Professional Development Strategies in the Conditions of Digitalization of Education (Foreign Experience Analysis)

The National systems of teacher’s professional growth play a special role in modern world education. At the stage of development of society, the teacher’s level of professional competencies directly affects the development of both the younger generation and the education system a whole. For the national systems of teacher’s professional growth to be constantly improved, and one of the main participants – the teacher – develops, it is important to choose strategies aimed at successful development. The article presents the experience of such foreign countries as Finland, Norway, and Latvia designing strategies for the teachers’ professional development in the context of digitalization of education. The authors of the article propose an analysis of modern international models of teacher’s professional growth as an opportunity to consider their advantages and disadvantages for designing similar domestic models.
Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training—The Case of Cancer Detection

The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper, we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and classification frameworks with various class distributions in the training set, such as natural, balanced, over-represented cancer, and over-represented non-cancer. In the case of cancer detection, the experiments show several important results: (a) the natural class distribution produces more accurate results than the artificially generated balanced distribution; (b) the over-representation of non-cancer/negative classes (healthy tissue and/or background classes) compared to cancer/positive classes reduces the number of samples which are falsely predicted as cancer (false positive); (c) the least expensive to annotate non-ROI (non-region-of-interest) data can be useful in compensating for the performance loss in the system due to a shortage of expensive to annotate ROI data; (d) the multi-label examples are more useful than the single-label ones to train a segmentation model; and (e) when the classification model is tuned with a balanced validation set, it is less affected than the segmentation model by the class distribution of the training set.
Testing Tool for Information Visualizations based on User Interactions

Decision-making has become a vital tool in any organization, evolving from a process based on experience and intuition to one increasingly established in data analysis. One type of specialized software for data analysis is that of visual representations for large data sets. Visual representations are critically important today as they enable effective exploration of a data set and facilitate the task of identifying patterns and drawing conclusions. Every day more decisions are made based on visual analysis through visual representations of large data sets. It is not only a quantitative but also a qualitative increase. Decisions are more critical and with more impact on society, the environment, and individuals. In this context, it is essential to develop new and better methodologies and tools that allow the visualization developer to ensure the correct functioning of visual representations and their interactions. To achieve this goal, we present a web platform that assists in visualization testing through user interactions. This platform is based on a previously published black box testing technique for information visualizations that uses regular expressions to represent the sequence of user interactions.
Peningkatan Literasi Digital Guru Guna Mengatasi Permasalahan Pembelajaran di Era Pandemi Covid-19

The Covid-19 pandemic is one of the great catalysts that spurs transformation in the world of education. This encourages the importance of understanding teacher digital literacy in responding to educational transformation during the Covid-19 pandemic. The purpose of this Community Service (PkM) is to provide information and increase teacher knowledge of digital literacy during learning during the Covid-19 pandemic. The PkM targets are teachers in the provinces of West Java, Central Java, East Java, and DIY. The activity carried out was to provide education about the importance of digital literacy in responding to the transformation of education during the Covid-19 pandemic. The stages of this activity consist of 1) planning stage; 2) preparation stage, 3) implementation stage, and 4) evaluation stage. Before and after the activity, the PkM team provided a digital literacy questionnaire. The results of the PkM activity showed a significant increase in understanding of digital literacy, which was 21.3%.
Chaos as a Construct in Psychology

This chapter recounts the principal uses of the construct of chaos in psychology; these are entwined with other nonlinear dynamical constructs, especially catastrophes and self-organization. Chaos theory and research is primarily concerned with seeing the consequences of nonlinearity accurately and finding patterns in dynamics. The growth in substantive applications of chaos in psychology is partially related to the development of methodologies that work within the constraints of psychological data. Investigations in cognitive neuroscience, psychophysics, clinical and abnormal psychology, group dynamics, and organizational behavior all utilize the chaos construct and supporting empirical research. The chapter summarizes the current state of the chaos construct in empirical research and theory in psychology. Trends in research indicate that human systems do not remain chaotic indefinitely; they eventually self-organize, and the concept of the complex adaptive system (CAS) has become more useful for describing the balance between order and disorder within a system. Chaos or turbulence is generally higher in healthy systems compared to unhealthy ones, and there seems to be an optimum level of variability for producing adaptive responses across many types of living systems. Current research in group dynamics is examining the connection between the behaviors of a CAS and how movements and autonomic and EEG responses synchronize among group members.