Reviewerly/exHarmony huggingface dataset:
Innovative Fusion Conceptual Design and Research of Demolition Pliers for Emergency Rescue

This study is initiated from the urgent demand for the emergency rescue equipment when emergency rescue tasks are performed for public emergencies such as earthquakes and road traffic accidents. The characteristics of public emergencies and the development status of existing emergency rescue equipment are analyzed. It is of great research significance to propose that reasonably designed emergency rescue equipment can effectively reduce the casualties of rescuers and the loss of national property.BR The purpose of this research is to complete the design of a demolition device that is efficient, portable, easy to operate, reasonable in human-computer interaction, and has strong endurance. As an indispensable type of emergency rescue equipment, demolition equipment should be optimized and innovatively designed. Based on the analysis of the research status of demolition equipment and key technologies, it is proposed that there are still many problems that lead to the low application of demolition equipment in large disaster sites. This paper proposes to improve the driving method, structure function and interactive experience of the demolition equipment, so that the demolition equipment can achieve better use effect and have a better use experience, and reasonably realize the innovative fusion design scheme of the new demolition pliers integration.BR The research content and results are as follows: firstly, the purpose and method of the research are clarified through investigation and analysis; the second part is to clarify the design principles of the demolition equipment; the third part applies the design principles to complete the design of the demolition pliers; the fourth part applies the relevant national standards and policies The document carries out the verification analysis on the design scheme. Based on these research results, we hope to provide some design inspiration and research ideas for the future innovative fusion design of emergency rescue equipment.
A Study on Solar Irradiance and Lux Illuminance: Conversion Guide

This study establishes a conversion standard between solar irradiance and lux illuminance.&nbsp; With this conversion, a solar researcher can utilize a low-cost light meter to measure solar irradiance light spectra. The standard for measuring solar irradiance utilizes the units of watts per meter squared (W/m2). Irradiance meters are both costly and limited in the ability to measure low irradiance values.&nbsp; With a lower cost and higher sensitivity in low light conditions, light meters measure luminous flux per unit area (illuminance) utilizing the units of lumens per meter squared or lux (lx). An effective conversion factor between W/m2 and lx would enable the use of light meters to evaluate photovoltaic performance under low solar irradiance conditions. A survey of the literature found no definitive and readily available "rule of thumb" conversion standard between solar irradiance and illuminance.&nbsp; Easy-to-find Internet sources contain conflicting and widely varying values ranging from 688449 to 21000 lx for 1000 W/m2 (1 Sun) of solar irradiance. Peer-reviewed literature contains Luminous Efficacy equivalent values ranging from 21 to 131 lx per W/m2. This manuscript explores the relationship and establishes a theoretical and laboratory measurement guide for converting solar irradiance and illuminance. The conversion factor includes standards data, equipment calibration accuracy, and uncertainty estimates. Solar Irradiance of 1 Sun (1000 W/m2) for an LED-based solar simulator is (116 ± 3) klx and (122 ± 1) klx for outdoor sunlight.
Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming

Search-Based Software Engineering problems frequently have semantic constraints that can be used to deterministically restrict what type of programs can be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Programming by preventing solutions that are known to be invalid from ever being added to the population. However, the restrictions in real world challenges like program synthesis, automated program repair or test generation are more complex than what context-free grammars or simple types can express. We address these limitations with examples, and discuss the process of efficiently generating individuals in the context of Christiansen Grammatical Evolution and Refined-Typed Genetic Programming. We present three new approaches for the population initialization procedure of semantically constrained GP that are more efficient and promote more diversity than traditional Grammatical Evolution.
Internationalization of Higher Education in Russia: Regional Perspective

The risks of non-fulfillment of the target indicators of the federal project “Export of Education”, which are systemic in nature and are associated with the peculiarities of the socio-economic, cultural development of Russian regions and their conditions for implementing the internationalization of higher education, have undergone a transformation during 2020-2021 in the context of a pandemic and active use of distance technologies and electronic educational resources. This placed the emphasis on the level of competitiveness and the development of these technologies in comparison with large international providers - foreign universities. The goal of this paper is to identify and substantiate internationalization features of Russian higher education at the regional level and to forecast further scenarios of development in the context of a long term impact on public policy in this area. The research revealed and systematized the features of the internationalization of higher education at the regional level with their division into positive, negative, and crisis features. Trends in the number and structure of foreign students studying in Russia were identified and analyzed. The calculation of the effective rating of foreign students by importing countries was carried out. The authors developed and tested a six-factor function for assessing the level of internationalization of the university. The paper presents a model of interaction between regional universities and universities participating in the implementation of the Federal project “Export of Education”.
DESIGNING “VERBHOUSE” TEACHING MATERIALS TO SUPPORT TEACHING ENGLISH VOCABULARY IN JUNIOR HIGH SCHOOL

This study aimed at designing a multimedia-based product as supplementary material. It is known as VERBHOUSE. The product purposes were to help an English teacher in SMP Negeri 1 Anjongan teach English Vocabulary, especially English action verbs for year-8 students, and find out if it is suitable in supporting the English teachers. The product materials were designed based on “Bahasa Inggris for year-8 students in Junior High School” textbook. Moreover, the researcher gathered the data through an interview and that was supported with open-ended questionnaires. In analyzing the data, the researcher used an internal analysis. The researcher had developed the product within three phases of ADDIE: Analysis, Design, and Development. The researcher conducted the interview and distributed the questionnaire in the Analysis phase. The researcher then started to design the first draft in the second phase. VERBHOUSE consisted of four main menus, House, Exercises, Guide, and Credit. The product contained 40 English action verbs materials, exciting pictures, sounds, video, and provided explicit texts to the user. Furthermore, in the development phase, the researcher used an internal evaluation to measure the product. From the findings, it is indicated that the VERBHOUSE is suitable for classroom use. In short, this study has relatively succeeded in creating multimedia materials for year-8 students as supplementary materials.
Event-based Extraction of Navigation Features from Unsupervised Learning of Optic Flow Patterns

We developed a Spiking Neural Network composed of two layers that processes event-based data captured by a dynamic vision sensor during navigation conditions.The training of the network was performed using a biologically plausible and unsupervised learning rule, Spike-Timing-Dependent Plasticity.With such an approach, neurons in the network naturally become selective to different components of optic flow, and a simple classifier is able to predict self-motion properties from the neural population output spiking activity.Our network has a simple architecture and a restricted number of neurons.Therefore, it is easy to implement on a neuromorphic chip and could be used for embedded applications necessitating low energy consumption.
Secured IoT Device Management in Cloud-Fog Environment using Blockchain

The integration of Internet of Things (IoT) with cloud and fog computing makes people’s lives simpler. Resiliency is measured in order to observe how the IoT-cloud-fog environment will cope with the changes when any of the devices is compromised. The blockchain is essentially an append-only data structure that records all the activities in transactions and creates an immutable and distributed ledger of blocks when all participants in the peer-to-peer network agree without requiring trust on a central authority. The chapter discusses the performance evaluation of our proposed model. It emphasizes the need of the blockchain to solve key compromise problem. Using the Balanced Incomplete Block Design (BIBD) model, the keys are generated and efficiency of the network is evaluated. BIBD key generation is done using JMP tool and imported into python for key distribution and gateway selection. To overcome this problem, we deployed a blockchain-based solution to protect the compromised node, which is not achievable using the BIBD model.
Multiple imputation using the average code from autoencoders

Background: Missing information is a constant issue in the clinical setting. The presence of missing values (MV) is triggered by the wrong acquisition of data or sudden events in the patient’s health condition. Imputation arises to replace the non-existent information with the twofold purpose of benefiting from existing information and reducing bias in clinical settings. Mechanisms based on deep learning and multiple imputation (MI) are leading alternatives to impute MVs because of their capacity to extract complex relationships and the consideration of uncertainty that MI adds. Objective: This study aims to improve the reconstruction of missing information through a novel imputation alternative that integrates a MI paradigm into deep learning models. Methods: The proposed method integrates the MI paradigm into the latent representations of an autoencoder, the so-called codes. The average code is then computed, boosting a better latent representation of data. Finally, the average code is decoded to reconstruct MVs. Results: The proposed method is tested in 6 datasets with different patters of MVs. It is compared with solutions based on autoencoders and generative adversarial networks. For the random appearance of MVs, the proposed method outperforms 97% of the scenarios with a reconstruction gain that ranges 1.04-1.45. For the other MVs mechanisms, the proposed method improves the reconstruction in at least 69% of the experiments, with a gain of 1.13-1.91. Conclusion: The findings of the proposed approach showed that the reconstructive capacity of the average code outperforms in most of the scenarios its competitors and close to the best solution in the rest of the scenarios. The integration of the MI paradigm into latent representations of data and the computation of average codes allow a more robust representation of the data and enables the enhancement of current state-of-the-art methods for high MVs rates.
11. Dense CNN approach for medical diagnosis

Healthcare is one of the domains which has truly advanced to use the latest technologies like machine learning to help in diagnosis. Owing to its complexity of medical images, extracting features correctly makes the problem even tougher. The earlier image processing algorithms using descriptors were unable to detect the disease accurately, and also, using the correct form of descriptors based on the dataset was even a bigger challenge which further reduced the accuracy of the machine learning algorithms trained over the dataset. However the recent advancements in the field of deep learning are able to give better results for these classifications. Convolutional neural networks (CNNs) have proved to be a great algorithm choice in case of extracting spatial features, making it suitable for the medical diagnosis. However, as the number of layers increase in CNNs, the complexity of the network increases and the information passed from one later to another eventually decreases, thus causing information loss. In order to overcome this dense CNN can be considered. We have shown couple of case studies which were performed. One using the local binary patterns and other using dense CNN on two different types of medical images. The dense CNN work was also recognized by the IEEE Computer Society.
Blockchain in Artificial Intelligence

Blockchain and Artificial Intelligence (AI) have propelledadvances and growth that have resulted in development across pretty much every industry. AI alludes to machines that are worked to perform shrewd errands that have generally been refined by people. Blockchain is a decentralized organization of PCs, which records and stores information to show an ordered arrangement of occasions on a straightforward and permanent record framework. AI and blockchain are ending up being an incredible amazing pair, improving pretty much. It is evident that AI and blockchain ideas are spreading at a wonderful rate. The two innovations have a particular level of mechanical intricacy and multidimensional business suggestions. In any case, a typical misconstruing idea about blockchain, specifically, is that “a blockchain is decentralized and accordingly nobody controls it.” In any case, the fundamental improvement of a blockchain framework is as yet credited to a group of center designers. Accept keen agreement, for instance, it’s anything but an assortment of codes (or capacities) and information (or states) that are modified and sent on a blockchain (say, Ethereum) by various human software engineers. It is, hence, shockingly, less inclined to be liberated from escape clauses and imperfections. Regardless of the sector in which they are used, anything from grocery store network coordinations and medical records exchange to media sovereignty and monetary security is being revamped with blockchain and AI. Blockchain–AI assembly is inescapable on the grounds that both are arrangements with information and worth. Blockchain empowers secure capacity and sharing of information or anything of significant worth. AI can examine and create bits of knowledge from information to produce esteem.
Primena kriptografskog modela zaštite podataka u grupnom radu studenata

The aim of this study is to present a data protection cryptographic model which can assist students in making decisions within group work. Using group work in assessing students has been increasing recently, due to the growing use of the Internet in teaching, especially during the COVID-19 pandemic which made group work shift to the online form. In order to prevent possible copying and disagreements between group mem- bers, it is necessary to develop a system that will protect data, but also allow group members to reach an agreement in a voluntary, solidary and democratic way. One of the possible solutions is the use of cryptography. For the purpose of developing a cryptographic model, Galois fields GF(256) were used and mathematical operations in the polynomial field in GF(256) were developed. The Lagrangе interpolation polynomial is then implemented in the model, which allows the teacher to generate ordered pairs that are part of the key. In this paper, such a model is developed and a pseudo algorithm for the given model is presented. In addition, an example of application of the model in teaching programming is given.
AN INVESTIGATION INTO THE COMMON PRONUNCIATION ERRORS MADE BY YOUNG LEARNERS

This paper aims to investigate the common pronunciation errors of young learners who study English at a language school as their extra-curriculum. This study adopted the qualitative methodology, using voice recording to allow a deeper insight into the reality of pronunciation errors made by these students. There are 50 participants (aged from 11 to 15 years old) who are studying in Language School A – Branch 2. The students had to read one paragraph in the text book aloud and the researcher recorded their voice. To make the accuracy of the study, the students have already known how to pronounce the words that are used to test the students’ pronouncing ability the research. Based on the analysis, the sounds that were commonly mispronounced by the students were long vowel sounds, plosives consonants and the final consonants. This study will be a useful resource for both teachers and students to enhance the teaching and learning with regard to phonology aspect. However, the small size of dataset means that it is not possible to generalize the whole situation and thus another study with a larger size of sample should be carried out to find out a more accurate answer for the problem.
The Autonomous Learner Model

The Autonomous Learner Model incorporates the needs of the whole gifted child, looking at giftedness and potential. The five dimensions of the Autonomous Learner Model are: Orientation, Individual Development, Enrichment, Seminars, and In-Depth Study along the perimeter. Many teachers at the elementary level find it easy to integrate activities from the Orientation Dimension in class meetings, opening or closing activities, or during brain breaks. The Individual Development Dimension of the model provides learners with the opportunity to develop the cognitive, emotional, social, and physical skills, concepts, and attitudes necessary for lifelong learning. When learners engage in the Enrichment Dimension, they have explored their giftedness through the Orientation Dimension and have a foundational understanding of who they are as learners through activities in the Individual Development Dimension. The richness of an In-Depth Study is that it is learner-driven through a proposal process, and the facilitator coaches the learner while the research is conducted.
The Problem of Monotonicity and the Skeleton

The premise p of a reasoning is usually a complex statement reflecting the information of departure and consisting in the conjunction of other statements, p = p1 · (p2 · (…(pn). Such p can be written without parenthesis provided conjunction is associative, and then with the possibility of placing the sub-indexes in any ordering if it is commutative; on the contrary neither parenthesis, nor ordering can be avoided.
Management of an educational organization based on monitoring studies

The article is devoted to the management of an educational organization on the basis of monitoring research. The study has a topic that is relevant in the conditions of our time, since today the state gives priority to supporting education, in this regard, the municipal education system needs to ensure the effective use of all its resources: information, material, human and financial. When considering the indicated problem, the methods of systematization and generalization were used, allowing to consider this problem, taking into account many factors affecting the quality management of educational activities on the basis of monitoring studies. The article highlights the main approaches to the process of assessing the quality of educational activities using the electronic information and educational environment, implemented by higher educational institutions. The practical significance of the obtained research results lies in the fact that the conclusions drawn and the proposed scientific and theoretical provisions can be useful to the heads of educational organizations and have the ability to adapt to the conditions of the educational process. The article will be useful for administrative and pedagogical staff of schools, as well as for everyone interested in researching the issues of quality management of education and educational services based on monitoring studies.
Attendance Maintaining and Monitoring using Face Recognition

Technology has been playing a vital role in this world, where the work and the work place become digitalized. The paper reviews on monitoring the attendance using image processing, which involves face detection, labeling the detected face, training a classifier based on labeled dataset, and face recognition. Former methods on monitoring the attendance includes signing the attendance registry, fingerprint detection and barcode scanning where delinquency may occur. To prevail over and to take the technology to subsequent level image processing has been incorporated. Proposed system employs, capturing of the face in various dimensions, labeling of the captured images that is stored in the database for training and testing phase. Using the gathered data the machine is trained to recognize the face to provide access to the employees or students in the organization. The final phase is to take the attendance and maintain the record on attending hours using face recognition technique in which the input image of the employees or students is given.
Time Series Forecasting Using Long Short-Term Memory Neural Networks: A Case Study of Seismogram

This paper presents an investigation of the long short-term memory (LSTM) neural networks for seismogram time series prediction. LSTM has been widely used for time series prediction problems and achieved excellent results therefore it is interesting to utilize this technique for seismology. A seismogram from Albuquerque, New Mexico (Anmo), USA provided by the IRIS website is used for the experiment. The seismogram is recorded to observe passive seismic activities like Earthquake. The data is recorded with a 16 kHz sampling frequency by a station in Albuquerque. The network is with 200 hidden units and trained with 250 maximum iterations. The LSTM is able to achieve 0.00306 of root mean squared error (RMSE).