Reviewerly/exHarmony huggingface dataset:
A deep learning method based on language models for processing natural language Russian commands in human robot interaction

The development of high performance human-machine interface systems for controlling robotic platforms by natural language is a relevant task in interdisciplinary field «Human-Robot Interaction».In particular, it is in demand, when the robotic platform is controlled by an operator without any skills necessary to use specialized control tools.The paper describes a complex Russian language commands processing into a formalized RDF graph format to control a robotic platform.In this processing, neural network models are consistently used to search and replace pronouns in commands, restore missing verbs-actions, decompose a complex command with several actions into simple commands with only one action and classify simple command attribute.State-of-the-art solutions are applied as neural network models in this work.It is language models based on deep neural networks transformer architecture.The previous our papers show synthetic datasets НАУЧНЫЙ РЕЗУЛЬТАТ.ВОПРОСЫ ТЕОРЕТЙЧЕСКОЙ Й ПРЙКЛАДНОЙ ЛЙНГВЙСТЙКЙ RESEARCH RESULT.THEORETICAL AND APPLIED LINGUISTICS based on developed generator of Russian language text commands, data based on crowdsourcing technologies and data from open sources for each of the described stages of processing.These datasets were used to fine-tune the language models of the neural networks.In this work, the resulting fine-tuned language models are implemented into the interface.The impact of the stage of searching and replacing pronouns on the efficiency of command conversion are evaluated.Using the virtual three-dimensional robotic platform simulator created at the National Research Center «Kurchatov Institute», the high efficiency of complex Russian language commands processing as part of a human-machine interface system is demonstrated.
THE IMPORTANCE OF DEMOCRATIC MANAGEMENT IN THE ORGANIZATION OF EDUCATIONAL WORK

This article aimsto investigate the manager's relationship in the organization of the pedagogical work with the actors of the educational process in a School in the Municipality of Boa Vista, State of Roraima.For this purpose, the methodology used was bibliographical and field research.In order to carry out a survey of data collection on how the manager's performance is seen by the agents that make up the school environments.A questionnaire was applied, with structured questions of the open semi-open type, in addition, through a theoretical basis, an explanation will be carried out regarding the pedagogical management and the perspectives of this mode of management in Brazil.
ISFRNet: A Deep Three-stage Identity and Structure Feature Refinement Network for Facial Image Inpainting

Modern image inpainting techniques based on deep learning have achieved remarkable performance, and more and more people are working on repairing more complex and larger missing areas, although this is still challenging, especially for facial image inpainting.For a face image with a huge missing area, there are very few valid pixels available; however, people have an ability to imagine the complete picture in their mind according to their subjective will.It is important to simulate this capability while maintaining the identity features of the face as much as possible.To achieve this goal, we propose a three-stage network model, which we refer to as the identity and structure feature refinement network (ISFRNet).ISFRNet is based on 1) a pre-trained pSp-styleGAN model that generates an extremely realistic face image with rich structural features; 2) a shallow structured network with a small receptive field; and 3) a modified U-net with two encoders and a decoder, which has a large receptive field.We choose structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), L1 Loss and learned perceptual image patch similarity (LPIPS) to evaluate our model.When the missing region is 20%-40%, the above four metric scores of our model are 28.12,0.942, 0.015 and 0.090, respectively.When the lost area is between 40% and 60%, the metric scores are 23.31,0.840, 0.053 and 0.177, respectively.Our inpainting network not only guarantees excellent face identity feature recovery but also exhibits state-of-the-art performance compared to other multi-stage refinement models.
Factors affecting the use of accounting and finance technology during the pandemic crisis

The recent COVID-19 pandemic, which led to lockdowns and new working norms, has influenced business and accounting transactions in significant ways. This phenomenon and its longer-term impacts are still under-researched and remain unexplored in emerging countries, particularly Malaysia. Therefore, the current study intended to determine the actions taken by Malaysian organizations, specifically in their accounting and finance functions, in response to the COVID-19 crisis. Online survey questionnaires were distributed to collect data. The questionnaires comprised several sections, including demographic information of the respondents and factors affecting users’ intentions to use technology for accounting and finance functions in the face of the COVID-19 crisis. This research adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical basis from which to evaluate the research objectives. The findings showed that only performance expectancy is related to user intention, while other factors, such as effort, attitude, social influence, self-efficacy, and anxiety, display the opposite effect. This empirical study suggests that accounting and finance functions in Malaysia still lag behind in terms of the technology used by employees. In addition, organizations, particularly government departments, are not ready to adopt a fully integrated scientific accounting and finance system.
Digital Copyright Law, A Comparative Study of the Limitation and Exceptions Relating To Education, Betsy VinoliaRajasingh.: Thomson Reuters First Edition, 2020. 348, Price: Rs. 750/‐, ISBN 978‐93‐89891‐35‐5.

The data that support the findings of this study are openly available in Manish Kumar at https://official.link/Manish-kumar.
Optimal Design of 20 KW Grid-Connected Solar Power Plant for Maximizing Solar Radiation in Tabriz utilizing PV syst Software

In this paper, an optimal method for technical studies and feasibility studies for the construction of a photovoltaic power plant connected to the grid with a capacity of 20 kW is presented to design an optimal angle according to the geographical coordinates of the power plant construction site. Photovoltaics (PV) arrays and inverters are one of the main pieces of equipment. are in this field. Choosing the right capacity, layout and angle increases the efficiency of the power plant and also reduces the cost and losses. Based on the geographical location of the solar power plant and also the placement of PV arrays for ease of maintenance and creating the optimal angle of PVs, received the maximum power from sunlight is realized.
Feature Generation Model for Imbalanced Classification Problems

Class-imbalance learning deals with the problem where the number of training data in one class is significantly more than another class. To tackle this problem, some feasible and effective methods have been proposed in recent years. However, most of them are simply designed and cannot solve the essential difficulties embedded in the imbalanced data classification like noises and class overlapping. These problems make learning imbalanced data more difficult, but existing algorithms tend to ignore it and directly utilize intuitive class-wise resampling or reweighting to balance the dataset. To address these challenges, we propose a feature generation model, which mainly contains two parts: competitive learning and feature transformation. The experiments result on eight class-imbalance datasets show that the proposed method outperforms state-of-the-art approaches in class-imbalance learning task.
Educational Metaverse Dilemmas and Solutions: a stakeholder-based perspective

The education metaverse has sparked a lot of attention as a new model for online education. But in the practical application of educational metaverse, there are often some contradictions and conflicts among multiple stakeholders, including the government, scientific and technological enterprises, and educational institutions. A challenge that needs to be resolved is how to build a high-quality and effective education metaverse environment while making sensible use of the new technological features of the education metaverse. Therefore, this article explores the basic connotation of the education metaverse from the perspective of stakeholders, discusses the current dilemmas faced by the education metaverse, and finally provides relevant solutions and advice to solve the problem.
Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data

Computer science major in secondary vocational schools focuses on cultivating students' ability to solve complex engineering problems. Thus both theoretical and practical teaching of professional courses are critically important. Due to the rapid update of knowledge in the computer domain, it is becoming challenging to better integrate theory and practice knowledge for teaching with advanced intelligent information technology. This paper constructs the knowledge graph for the computer networking course group in secondary vocational schools. We first propose methods for data acquisition, keyword extraction, relationship extraction, and knowledge storage. Then, we describe the complete process for constructing the knowledge graph. Finally, we visualize the relationship between knowledge points for theoretical and practical courses with the tool SmartKG. Our proposed method provides new ideas for teaching computer courses in secondary vocational schools, which helps cultivate high-quality skilled talents.
CMHS:Feature selection based on harmony search algorithm with cosine and momentum control factor

Selecting the optimal subset from high-dimensional features can improve the algorithm’s efficiency and performance for data mining tasks while also avoiding the overfitting phenomenon. Some works have improved the harmony search(HS) algorithm to handle the challenge of finding the appropriate feature subset. These algorithms, however, are still suffering from an imbalance between global optimization and convergence accuracy. In this paper, we propose and evaluate a harmony search algorithm based on cosine and momentum control factors (CMHS) for feature selection. We incorporate the cosine similarity between harmonic vectors into the dynamic smoothing adjustment strategy of the HS’s control parameters and design a novel fitness function to overcome the mentioned problem. We compared the accuracy of seven UC datasets side by side, and the average accuracy of CMHS is 96.49%, higher than the 93.78% of NGHS and the 93.33% of IBGHS. Moreover, when compared to NGHS and IBGHS, the fitness value of CMHS decreases by 25% on average, indicating that our proposed improvement strategy strikes a reasonable balance between global optimization and convergence accuracy, allowing CMHS to display more steady performance in the feature selection task.
Individual Identification Algorithm of Known Emitters Based on Lightweight Binary Channels Neural Network

Aiming at the problem that the traditional specific emitter identification method has poor feature extraction ability and limited recognition effect, this paper proposes a known specific emitter identification algorithm based on the LBCNet (the Lightweight Binary Channels Net). The algorithm uses bispectrum and cyclic spectrum to highlight the unintentional information in the pulse. Then, it combines the idea of depthwise separable convolution to design the LBCNet to realize adaptive feature extraction, fusion, and classification. Finally, it integrates the attention mechanism to highlight the effective information of the image, enhance the neural network’s learning ability and improve the identification performance of specific emitters. The simulation results show that the recognition rate of the proposed algorithm is as high as 94.53% at 0dB, which has good engineering practical value and can be applied to the field of electronic reconnaissance.
SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications.Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system.This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background.In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data.Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion.Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.
Students’ information competence and their value orientations

The article analyzes the radical changes that the information revolution has made in the field of education. The emergence of a new educational paradigm involves understanding the goals and means of socialization of the individual in the information society. Information competence of a student becomes the most important condition for his/ her professional and personal self-fulfillment. The most important component of information competence is the value system, which is formed in a consolidated process of education and upbringing. Our time brings to the fore the study of the socio-cultural aspect of human existence in the information environment. In the process of historical development, the ways of storing and transmitting information have changed (from oral to written, then printed and electronic ones). The next stage in the development of information culture organically included the previous one, reworking it. The information society is becoming a new environment for human existence. The author shows that the former educational paradigms have always been based on ideas about human development through education, which included communication. In modern society, the communication process becomes multidirectional, including, in addition to personal contacts, various information carriers, which are university courses, educational portals, electronic encyclopedias, YouTube channels, etc.
The significance and role of digitalization in the formation of universal culture and its effect on modern society

The article examines the significance and role of digital culture in the formation of universal culture and its impact on modern society. The development and contradictory nature of the influence of digitalization is analyzed. The research is based on the analysis of scientific and popular science sources. The article shows that the formation of digital culture as an independent industry is attributed to the second half of the XX century. First of all, this is due to the creation of a personal computer and other digital devices, the emergence of the global Internet as a mass form of communication, and also with the introduction of various practices of using computer technologies into the daily life of each of us. The author comes to the conclusion that the formation of such a new type of culture as digital should be based on systematic and integrated approaches.
A Proposed Hybridized Model for Image-Based Virtual Reality in Technology-Driven Self-Therapy

Stress is one of the issues in mental health among the societies. Self-therapy has been an alternative to provide relaxation and to reduce stress that includes the use of guided imagery therapy (GIT). It could also take advantages of technologies that support the sense of presence. In this technology-driven era, Spatial Presence Model (SPM) has been applied in existing studies to develop virtual reality (VR) tools, while Guided Imagery Therapy (GIT) has been used as a treatment tool for potential psychological problems. Currently, no study has utilized both SPM and GIT in supporting the practice of self-therapy to reduce stress. Hence, this study aims to propose a hybridized model for Image-based VR (IBVR) that incorporates SPM and GIT for the purpose of technology-driven self-therapy. The Design science research methodology (DSRM) is used as the basis for conducting the study. The proposed model is expected to benefit the application designers as a reference in developing IBVR tools for self-therapy.
IMPACT OF BIG DATA ON FINANCIAL REPORTING: MEDIATING ROLE OF AIS IN JORDANIAN FINANCIAL FIRMS

This study primarily examined the mediating role of the Accounting Information System (AIS) on the relationship between big data and financial reporting, with big data considered to have a significant effect on financial reporting.The required data were gathered from financial SMEs employees in Jordan, numbering 218, after which data was analyzed using SmartPLS.Based on the analysis results, AIS plays a mediating role in the relationship between big data and financial reporting, and big data has a significant positive relationship with financial reporting and AIS.The findings also showed a significant positive relationship between AIS and financial reporting.The study findings contribute to practice by providing empirical evidence of the factors that affect financial reporting in the financial sector firms in Jordan.The study also has its limitations, with one being the sole focus on a single context, which may not be representative of the behavior of SMEs all over the globe.Also, the study focused on limited factors and thus, future studies are suggested to incorporate additional variables and to obtain perspectives of other stakeholders.
A Study of Peer Review in English Writing from the Perspective of Interpersonal Function

In view of the problem that the sentence evaluation mechanism of correctionnet focuses on grammar and vocabulary modification, this paper, based on Halliday's interpersonal metafunction theory framework, sets up a peer assessment scale from three aspects: modal verbs, modal adjuncts and evidentiality.Taking English majors in provincial universities as the test subjects, this paper makes a comparative analysis of their interpersonal functional language representation data before and after training.The results of the study verified the applicability and popularization of the scale, and complied with the demand of combining online and offline teaching methods to improve learners' English writing ability in the data age. Research background and raising of problemsPeer assessment/peer feedback refers to that peers give constructive feedback, praise or guidance according to a set of standards provided by teachers to help improve your articles.The change of the concept of "student-centered" in classroom teaching, the change of the focus of "process teaching method" in writing teaching, and the change of the role of teachers to "non-authority" and "facilitator" have made peer assessment since the introduction of second language writing teaching (Elbow, 1973) in the 1970s, which aroused the interest of native English researchers, arouse students' enthusiasm for learning Because of its initiative and criticality, it is still a research hotspot in second language writing at home and abroad.On the one hand, the study of peer assessment involves the psychological, cognitive, emotional, social and cultural factors of teachers and students; on the other hand, it involves the setting of texts and evaluation criteria.This paper aims to address the problems that students have reflected in online composition correction on www.pigai.org, and to set evaluation criteria at the discourse level that can help offline students to evaluate themselves and each other, with a view to improving students' writing ability as a whole.[1] Theoretical BasisThe core components of the former Soviet psychologist Vygotsky's "cultural and historical development theory", namely, Zone of Proximal Development, Activity Theory and Mediation, provide the relevant theoretical basis for the research on the evaluation mechanism of this study.
Using Gestural Interaction Technology to Improve the Consumer Experience: An Abstract

Nowadays, creating a positive experience is a key source of competitive advantage (Lemon & Verhoef, 2016). A good experience makes a person five times more likely to recommend a company and more likely to purchase in the future (Yohn, 2019). Besides, gesture interaction technology appears as a promising way to provide individuals a global richer experience than with classical user interfaces (Daugherty et al., 2015). However, we currently ignore the concrete impact of this type of interaction on the consumer experience, whereas it is necessary for an efficient adoption of such interfaces. In this work, we try to get a better understanding of the role of the gestural interaction on the consumer experience, through qualitative and quantitative approaches. In-depth interviews were conducted with consumers to collect data in natural settings (Miles & Huberman, 1994). In order to triangulate the methods, we combined these interviews with two observation phases. The first took place with the same interview participants once the interview was over. The second observation took place in another study carried out a posteriori with different participants. As part of the latter, we built an interface based on the IKEA website and functioning by interacting with mid-air hand gestures, via the LeapMotion controller. During this second session, the participants also answered a questionnaire, allowing us to carry out a first quantitative exploratory study. In terms of main results, we were first able to observe a link between immersion and impulse buying, for example via a participant declaring to regularly scroll for hours to buy things that she does not need. We have also seen a connection between these purchases and the intuitive and natural aspect of gesture interaction. The latter can be explained through the presence (Ning et al., 2012; Vonkeman et al., 2017) which would be increased within the framework of this gestural interaction, but also via the perceived ownership, which increases the perceived valuation of an object by touching it virtually (Peck & Shu, 2009; Peck et al., 2013; Brasel & Gips, 2014). From a theoretical point of view, this work highlights the importance of affective consumer responses in the understanding of the gestural interaction experience. We also shed light to specific dimensions such as the intuitiveness, perceived control and immersion, and we propose a model explaining their influences. From a practical point of view, we show the crucial role of gestural interaction experience in particular touchpoints of the customer journey and consequently on the consumer experience.