Reviewerly/exHarmony huggingface dataset:
Pedagogical features of the ethnocultural aspect of personality in literature

The main goal of the study is to determine the main pedagogical aspects of the ethnocultural characteristics of the individual in the literature. There is a need for a cross-cutting humanization of education, strengthening the personal dimension in pedagogical science and practice. Orientation towards a person (a child, a youth, a student, a well-trained specialist) should become the main goal and content of the education system of the 21st century. The person must be put in the first place. To this end, education should contribute to a person's awareness of his own roots, his place in the world, the assimilation by each individual of the ethnoculture of the community as a way of his being, so that in the future he will be able to enter into a dialogue with other cultures, move freely in their space according to the principle “from knowledge and respect for one's own culture begins to respect other cultures, the desire to study the signs, symbols, codes of another community, to understand their stereotypes of behavior, the peculiarities of the world perception and worldview - the basis of tolerance and unity. " The study was carried out using the following theoretical methods: systems analysis and synthesis, induction and deduction, comparison, classification, generalization and systematization, idealization and abstraction. As a result, the main pedagogical aspects of ethnocultural personality traits in literature were identified.
Word Level Language Identification of English-Punjabi Code-Mixed Social Media Text

Code mixing denotes using multiple languages in an utterance. It is clearly seen that code mixing is pervasive while people communicate over social media irrelevant of the mode being used. The fusion of languages makes it more challenging and requires consistent updates according to recent trends. The current paper addresses three approaches namely CRFs (Conditional Random Fields), Bi-LSTM (Long Short-term Memory) and CNNs( Convolutional Neural Networks). Firstly, for word-level language identification of code-mixed English-Punjabi text CRF based system uses lexical, contextual, character ngram, and special character features. Secondly, Recursive Neural Network namely Bi-LSTM with glove embedding is used for language identification and thirdly CNN with glove embedding is used for language identification. It is observed that CRFs is the best performing system with an f1-score of 0.96.
Observer-Based Multi-Instant Fuzzy State Estimation of Discrete-Time Nonlinear Circuits via a New Slack Variables Technique

This brief investigates the problem of relaxed observer-based multi-instant fuzzy state estimation of discrete-time nonlinear circuits. In contrast with the recent result whose observer gain matrices may be too many to be applied in practice, there is only an unique group of observer gain matrices required in this study while the conservatism can be reduced markedly by developing a new slack variables technique. The proposed slack variables technique is constructed on the basis of multi-instant homogenous polynomials so that much more recessive algebraic properties of the normalized fuzzy weighting functions can be excavated in order to remove those redundant positive definite constraints which bring about conservatism. Especially, those previous slack variables techniques belong to special cases of ours. Finally, a benchmark example is given to validate the progressiveness of the developed result.
The Effect of Quality Perception and Consumer Attitude on Purchasing Decision

The purpose of this study was to obtain explanatory findings that were tested on the effect of (1) quality perception and consumer attitude on purchasing decisions, (2) quality perception on consumer attitude, (3) quality perception on purchasing decisions, (4) consumer attitude on purchasing decisions.This study used a causal quantitative research design.Data were collected by questionnaire and analyzed by path analysis.The results showed that there was a significant effect of (1) quality perception and consumer attitude on purchasing decisions of 84.4%, (2) quality perception on consumer attitude of 76.5%, (3) quality perception on purchasing decisions by 50%, and (4) consumer attitude on purchasing decisions of 44.6% in consumers who purchased and consumed soy sauce ABC in Buleleng District.
A Modified Electrosurgery Unit Based on High Frequency Design with Monopolar and Bipolar Method

Electrosurgery Unit is some machine to human body surgery. Electrosurgery Unit is uses a high frequency to human body tissue surgery, that causes blood who came out can be dicrease during surgery. When using a conventional scalpel is something that is highly avoided because it will cause contraindications to lack of blood which will be very dangerous for the patient. The purpose of this study is to design a tool that is used to replace a conventional scalpel with a tool that utilizes high frequency (400 KHz) generated by the oscillator circuit. In this study. The researches used Monopolar method and used two modes Cutting and Coagulation in order to eliminate faradic effects on body tissues causes high frequency, there is the high frequency will be adjusted to the duty cycle which aims to obtain various types of surgery required by doctor which is cutting 100% on and coagulation 6% on 94% off where is set it with arduino program. In this study, the researchers took advantage of the type of heat effect produced by high frequencies which were concentrated at one point so that it could be used to carry out the process of surgery on body tissues so as to minimize the occurrence of large blood loss. The result of this study is will be centered at one point on an object there is show up the depth difference of the cutting mode and the wide large difference of the coagulation mode. The module design resulted the power lowest at 300Ohm ESU Analyzer setting is 30Watt and the highest at 300Ohm setting too is 68Watt. The module design resulted the power lowest at 300Ohm ESU Analyzer setting is 30Watt and the highest at 300Ohm setting too is 68Watt at mode Cutting and the highest and lowest Coagulation mode using impedance setting the same there is 300 Ohm is constant 3Watt power result’s. The weaknesses at this research is the power still lower then the Electrosurgery Unit where is in the hospital. It can be fix for the next research using increase the final amplifier and maybe increase the frequency where is gonna make the voltage of the final amplifier be much better then this research
Visual Analytics for Information Security: Efficiency Assessment and Analysis of Visualization Methods

The purpose of the article: to identify and systematize information security problems that are solved using visual analytics methods, applied data visualization models and methods for assessing the effectiveness of visualization models. Research method: a systematic analysis of the application of visual analytics methods for solving information security problems. Analysis of relevant work in the field of information security and data visualization, as well as methods for assessing visualization. The objects of research are: solving information security problems through visual analysis and methods for assessing the effectiveness of visualization models. The result obtained: an interactive map of visualization models and their areas of application is presented, which will allow researchers and developers to choose the visualization models that are most appropriate for specific applied information security problems. A classification of methods for assessing visualization is presented. The scope of the proposed approach is the creation of visualization models that can be used to increase the efficiency of operator interaction with information security applications. The proposed article will be useful both for students studying in the direction of training “Information Security”, and for specialists who develop information security systems.
Fuzzy Logic Framework for Internet of Things Systems Implementation

This paper presents a framework to implement an Internet of Things (IoT) system using fuzzy logic. This framework provides general fuzzy membership functions that can be particularized for various applications implementing specific fuzzy rules to determine the system output. The motivation of this work is to create and test a flexible framework for the rapid implementation of IoT applications. Generic functions are implemented that are integrated into a commercial hardware platform. The framework is evaluated in terms of computation time and power consumption using a wireless sensor network (WSN) evaluation kit from Analog Devices. Also, a system for determining a person’s health condition is illustrated as an application implementation example using the proposed fuzzy IoT framework.
An In-depth Exploration on the Praxis of Computer-assisted Qualitative Data Analysis Software (CAQDAS)

NVivo is a methodological tool in analyzing qualitative data. The software belongs to the genre of Computer-assisted Qualitative Data Analysis Software (CAQDAS). This qualitative case study aimed to explore the practices of NVivo adopters within the West Visayas State University (WVSU) System, Academic Year 2019-2020. A researcher-made and foreshadowed Focus Group Discussion (FGD), in-depth interview, and observation protocols were utilized to gather data from the participants. The conceptual analysis was done using NVivo 12 Plus, while the researcher did the analytic analysis. The study revealed that the WVSU NVivo adopters are generally tech-savvy and stationed in the research office. Their speed of using the software varies. The adopters vary in terms of using NVivo- some were fast learners, while others were slow. They regularly ask questions from the researcher-resource speaker, though the NVivo manual of operation was distributed to them. Seven (7) themes have emerged that may optimize NVivo’s functionalities in qualitative data analysis. These themes are requirements for improved coding practices, the need for enhanced visualization skills; the importance of the knowledge on the NVivo research cycle; qualities of NVivo adopters; motivation in using NVivo; and practices for practical qualitative data analysis using NVivo. It is thus concluded that there is a need for WVSU administration to consider several mechanisms to improve the use of CAQDAS, specifically NVivo, in qualitative data analysis.
Improving IoT Federation Resiliency With Distributed Ledger Technology

Despite the rapid spread of Internet of Things (IoT) systems, the lack of interoperability between the systems is significantly hindering their business and societal potential. Moreover, a major challenge for wider interoperability is that the IoT systems can be owned by multiple independent entities, whose collaboration will need to be organised to ensure their interoperability. One approach for achieving this is to establish federations supported by Distributed Ledger Technologies (DLTs), as this enables interoperability between entities and collaboration between business platforms, thereby overcoming many technical and administrative difficulties. DLTs can provide the required transparency and immutability for management of the federations, thus increasing trust and reducing the risk of misbehaviour that could destabilise the federation. This paper presents two system dynamics simulation models, which demonstrate that the success of a federation (with or without DLT support) is inversely related to the short-term selfishness of its members, and we then proceed to show that DLTs can improve the feedback received by the federation members on their actions by promoting a common consensus, which in turn can make the federation more resilient.
Identification of Garbage in the River Based on The YOLO Algorithm

This paper discusses the identification of garbage using the YOLO algorithm. In the rivers, it is usually difficult to distinguish between garbage and plants, especially when it is done in real-time and at the time of too much light. Therefore, there is a need for an appropriate method. The HSV and SIFT methods were used as preliminary tests. The tests were quite successful even in close conditions, however, there were still many problems faced in using this method since it is only based on pixel and shape readings. Meanwhile, the YOLO algorithm was able to identify garbage and water hyacinth even though they were closed to each other.
Renewable Energy Certificate Trading via Permissioned Blockchain

With the continuous advancement of the green certificate trading mechanism, information verification needs to span multiple departments, which causes the application process cumbersome and human errors. In order to solve problems of cumbersome issuance process of the renewable energy certificate (REC) and the inflexible pricing mechanism, in this paper, a hybrid REC trading system was proposed based on an permissioned blockchain technology (BT), which combined advantages of the BT and the continuous double auction (CDA). The operation process of the system was introduced in detail, and the view change protocol in the Practical Byzantine Fault Tolerance algorithm was revised according to the characteristics of the system to improve the system stability. The continuous double auction rule was also introduced in the system. And corresponding bidding strategies were designed to maximize the revenue of users (buyer and seller) and transaction probability. The simulation experiment proves that the bidding mechanism can flexibly adjust the REC price according to the supply and demand relationship. At the same time, the effectiveness and feasibility of trading rule and bidding strategy were also verified.
Recent Advances in Zero-Shot Learning

Deep learning has shown excellent performance in the field of artificial intelligence. In the supervised identification task, deep learning algorithms can achieve unprecedented recognition accuracy by training massive tagged data. However, owing to the high cost of labeling massive data and the difficulty of obtaining massive data of rare categories, it is still a serious problem how to identify unknown class that is rarely or never seen during training. In view of this problem, the researches of Zero-Shot Learning (ZSL) in recent years is reviewed and illustrated from the aspects of research background, model analysis, data set introduction and performance analysis in this article. Some solutions of mainstream problem and prospects of future research are provided. Meanwhile, the current technical problems of ZSL is analyzed, which can offer some references to beginners and researchers of ZSL.
An English Teaching Ability Evaluation Algorithm Based on Frequency Effect

Background. English is one of the courses offered in all colleges and universities. The quality of English teaching is directly related to the quality of talent training and the development of students themselves. “Teaching quality evaluation” specifically refers to the education evaluation with teaching as the evaluation object. It is the core and foundation of the whole education evaluation. Teaching quality evaluation is based on certain teaching objectives and teaching norms and standards, through the systematic detection and assessment of teaching and learning. Evaluate its teaching effect and the degree of realization of teaching objectives, and use scientific and feasible methods to make corresponding value judgments to improve the process of teaching. To improve the accuracy of English teaching ability evaluation, an English teaching ability evaluation algorithm based on frequency effect is proposed. Methods. The paper proposes an English teaching ability evaluation algorithm based on frequency effect. Firstly, it constructs the evaluation index system of English teaching ability, including expert evaluation system, student evaluation system, and teacher evaluation system. Then, the indexes affecting the evaluation of English teaching ability are quantified by fuzzy synthesis, and the evaluation indexes are refined. Finally, the basic principle of frequency effect is analyzed, combined with the convolutional neural network. Results. The convolutional neural network evaluation model is constructed, the teaching ability indicators are input into the model, the final evaluation results are output, and the design of the English teaching ability evaluation algorithm based on frequency effect is completed. Conclusions. The experimental results show that this method has high accuracy and efficiency.
Cloud Data Integrity Verification Algorithm for Sustainable Accounting Informatization

In recent years, people have paid more and more attention to cloud data. However, because users do not have absolute control over the data stored on the cloud server, it is necessary for the cloud storage server to provide evidence that the data are completely saved to maintain their control over the data. Give users all management rights, users can independently install operating systems and applications and can choose self-service platforms and various remote management tools to manage and control the host according to personal habits. This paper mainly introduces the cloud data integrity verification algorithm of sustainable computing accounting informatization and studies the advantages and disadvantages of the existing data integrity proof mechanism and the new requirements under the cloud storage environment. In this paper, an LBT-based big data integrity proof mechanism is proposed, which introduces a multibranch path tree as the data structure used in the data integrity proof mechanism and proposes a multibranch path structure with rank and data integrity detection algorithm. In this paper, the proposed data integrity verification algorithm and two other integrity verification algorithms are used for simulation experiments. The experimental results show that the proposed scheme is about 10% better than scheme 1 and about 5% better than scheme 2 in computing time of 500 data blocks; in the change of operation data block time, the execution time of scheme 1 and scheme 2 increases with the increase of data blocks. The execution time of the proposed scheme remains unchanged, and the computational cost of the proposed scheme is also better than that of scheme 1 and scheme 2. The scheme in this paper not only can verify the integrity of cloud storage data but also has certain verification advantages, which has a certain significance in the application of big data integrity verification.
A Machine Vision for Automatic Hydrometers Calibration

Due to the changes imposed by Industry 4.0, the service market has also been changing, including metrological laboratories, where this adaptation is increasingly necessary.Andthis evolution has been called Metrology 4.0, where the automation of calibration processes is necessary, and with this these processes become more agile and less susceptible to human errors.The calibration ofHydrometers (glass densimeters) is a point of extreme importance for various sectors of industry and commerce, with this equipment it is possible to check the quality of products such as alcohol, petroleum derivatives, oils, milk among others.This project proposes the development of a device (including hardware and software) described as a computer vision machine (MVS) for automatic calibration of hydrometers by the Cuckow method using computer vision techniques.The results showed robustness and accuracy for the calibration task of glass densimeters by the proposed MVS and the other point to be highlighted is the measurement speed, where each measuring point took an average of 6 seconds to stabilize and final reading of the value.
Development of Tourism Villages Based on Promotion Integration Through Websites

Galengdowo Village has both natural and artificial tourism potential. The problem is that the promotion has not been carried out optimally and has not been integrated into one website and the lack of tourist information for prospective tourists. The goal of this action is to progress tourist villages through digitization and web sites. The methods used are training, mentoring, supervising, and asessment. The findings indicate this the tutoring and assistance in operating the web and the design of the promotion greatly impact the participants, it can regarded the finding of monitoring through the galengdowo. id web. he tourist information center is very much needed by the manager because it can provide information about tourist attractions to tourists. The results of the organizational management training, namely increasing the quality of Pokdarwis managers, which initially did not have a job description and after the training, there was a job description for each administrator
Analysis of Different Digital Alternatives as Teaching Tools to Improve the Teaching–Learning Process

Virtual tools are frequently used in education. Among them, the use of virtual laboratories could be an interesting alternative to strengthen the practical concepts of the students, especially in the current paradigm in which the presence of students is often not possible. For this reason, the aim of this study was to analyse the use of different digital tools for the improvement of the teaching process during the COVID-19 pandemic period. To this end, a comparison of the application of different digital alternatives was carried out, evaluating the differences found with previous teaching courses. The results indicate that, although students welcome these activities, they cannot replace face-to-face practices, being considered as a complementary activity.
Open source automatic lecture subtitling

We present a fully automatic solution for German video subtitling, with a focus on lecture videos. We rely entirely on open source models and scripts for German ASR, automatic punctuation reconstruction and subtitle segmentation. All training scripts, 1000h of German speech training data, pre-trained models and the final subtitling program are publicly available. It can readily be integrated into lecture video platforms such as Lecture2Go. The automatically generated subtitles can also serve as a basis to make the video material more accessible (e.g. via search, keyword clouds, and the like) or for further manual revision, potentially helping in significantly speeding up manual work. A particular challenge that we observe in lectures are technical terms that are frequent in a particular lecture, but infrequent in a typical language model and that might be out of vocabulary for a general purpose ASR. We approach this challenge by extracting texts from accompanying lecture slides to adapt the language model of our TDNN-HMM based ASR system. We demonstrate the usability of the full system and its generated subtitles and evaluate on a dataset of manually transcribed lectures with an average of 26.3% WER.
Autotuning of Exascale Applications With Anomalies Detection

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.