Reviewerly/exHarmony huggingface dataset:
Data Analysis using Data Mining Techniques as K Means and Apriori Algorithms

Data is major concern in today’s digitized world. Data mining is an important method to increase efficiency, discover hidden (novel), useful, valid and understandable knowledge from a massive database. It is the process of analysing data from different perspective and summarizing the data into a useful identical format of information that can be used to predict future trends or performances. The ultimate goal of data mining is to recognize pattern full information and predictions. The most famous algorithms are K means and Apriori algorithms. Such algorithms were created to extract what is called nugget of knowledge from large sets of data. There are several different methodologies to approach this problem: classification, association rule, clustering etc.
Evaluation of the Need for Obesity Prevention Education Programs through Analysis of Factors Affecting Student Obesity Factors in Seoul during the COVID-19 Pandemic

The purpose of this study was to investigate the student obesity factors during the COVID-19 pandemic and identify the needs for obesity prevention education program. FGI and online survey were employed for data collection. The sample of the study was teachers who manage obesity prevention education in Seoul schools. Seven teachers participated in FGI, while 236 teachers completed the online survey in November 2021. Contents analysis was performed for the data from FGI, while a multiple regression analysis was conducted for the survey data. As a result of the FGI, Changes in the social environment due to COVID-19 have been investigated as a major factor in student obesity. The results from the survey revealed that behavioral factors, social factors, and environmental factors significantly affected student obesity during COVID-19 pandemic, which corroborates with Social Cognitive Theory. Due to COVID-19, the peer group (t=—5.904) had a significantly negative influence on student obesity, while parents (t=4.935) made a positive efffect on student obesity (p＜0.01). This study provides the basis for the strategy to develop a student obesity prevention education program during the periods with the infectious diseases such as COVID-19.
A Study on Addiction and Isolation in Virtual Reality Tourism Experiences

In this qualitative research note, we focus on two aspects of virtual reality tourism that haven't been talked about much: the risk of addiction and the feeling of being alone. Virtual reality tourism research so far has mostly looked at how current knowledge and theories can be used to help people have a good experience with VR apps. To look at reviews and blogs, the researchers used an inductive method. It turns out that virtual reality tourist experiences have common threads, like a short-lived sense of seclusion and the addictive power of virtual reality. More research is needed to learn more about addiction and social isolation so that we can better understand what causes them, how they work, and what they can do to us.
Performance Analysis of Array Multiplier Using Reversible Logic

Minimization of power and area miniaturization is the most required criteria in today’s world. Reversible logic is one of the evolving ways out for low-power computing. Multiplication is the most fundamental operation since the existence of multiply and accumulate unit in most of the computing systems. Paper provides an analysis of latest advancements in designs of unsigned parallel multiplier using reversible logic.
JPEG column: 89th JPEG meeting

JPEG initiates standardisation of image compression based on AI. The 89th JPEG meeting was held online from 5 to 9 October 2020. During this meeting, multiple JPEG standardisation activities and explorations were discussed and progressed. Notably, the call for evidence on learning-based image coding was successfully completed and evidence was found that this technology promises several new functionalities while offering at the same time superior compression efficiency, beyond the state of the art. A new work item, JPEG AI, that will use learning-based image coding as core technology has been proposed, enlarging the already wide families of JPEG standards.
KYC Verification Using Blockchain

Abstract: The Know Your Customer process that exists today is inefficient and inconvenient for both banks and customers. The procedure is unnecessary and increases operating expenses. Furthermore, it offers little to no privacy protection to users. A blockchain-based method is proposed in this paper. Because documents are maintained by a centralised organisation, verifying KYC documents for multiple financial sectors is a time-consuming and insecure process. The proposed KYC system is a Blockchain-based decentralised system that can be used to establish proof of identity for an individual person. It is also a costeffective method, and the data stored on the decentralised application provides an additional layer of security.
An evaluation of privacy policies used in digital contact tracing apps for COVID-19

Background: After coronavirus disease 2019 (COVID-19) spread across the globe, quarantine regulations were supplemented by digital contact tracing initiatives in the form of mobile applications and other surveillance. This paper examines digital contact tracing on a global scale using English privacy policy information from mobile digital contact tracing applications (DCTAs) compared to guidelines on digital proximity tracing from the World Health Organization (WHO) published in May 2020.
A Lane-Following Autonomous Driving of an Inverted Pendulum Mobile Robot Robust to Heavy Pitch Motions

In a vision-based autonomous driving of a mobile robot, heavy pitch motions can significantly deteriorate the boundary, or lane-following, performance. Such motions can result from the high maneuvers of an inverted pendulum balancing robot. If the vision camera frequently tilts forward or backward according to the mobile platform’s pitch motion, it would fail to extract drivable regions from the image data. It would also fail to detect successive lanes or boundaries to determine local paths, which is mainly due to the vision camera’s extrinsic parameter variation. In this paper, we investigate a local path planning method that accounts for the heavy pitch motion of a mobile robot by considering the camera posture in real-time image processing. Fundamentally, this method is based on the vanishing point tracking and the pinhole camera model. The highlight of this paper lies in employing the vertical filter to exclude vertical outliers from the boundary data and also the image horizon filter to determine the valid vanishing point candidates. Indoor and outdoor experiments demonstrate the effectiveness of the proposed method.
A Novel Transfer Learning-Based Algorithm for Detecting Violence Images

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images.The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work.This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images.Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset.The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.
Grammatical Disambiguation: The Natural Language Linear Complexity Hypothesis

By combining concatenations of constant complexity with a strictly time-linear derivation order, the computational complexity degree of DBS (AIJ’01) is linear time (TCS’92). The only way to increase DBS complexity above linear would be a recursive ambiguity in the hear mode. In natural language, however, recursive ambiguity is prevented by grammatical disambiguation. An example of grammatically disambiguating a nonrecursive ambiguity is the ‘garden path’ sentence The horse raced by the barn fell (Bever 1970). The continuation horse+raced introduces a local ambiguity between horse raced (active) and horse which was raced (passive), leading to two parallel derivation strands up to and including barn. Depending on continuing after barn with an interpunctuation or a verb, one of the [-global] readings (FoCL 11.3) is grammatically eliminated. An example of grammatically disambiguating a recursive ambiguity is The man who loves the woman who loves Tom who Lucy loves, with the subordinating conjunction who. Depending on whether the continuation after who is a verb or a noun, one of the two [-global] readings is grammatically eliminated (momentary choice between who being subject or object).
EpiLoc: Deep Camera Localization Under Epipolar Constraint

Recent works have shown that the geometric constraint can be harnessed to boost the performance of CNN-based camera localization.However, the existing strategies are limited to imposing image-level constraint between pose pairs, which is weak and coarse-gained.In this paper, we introduce a pixel-level epipolar geometry constraint to vanilla localization framework without the ground-truth 3D information.Dubbed EpiLoc, our method establishes the geometric relationship between pixels in different images by utilizing the epipolar geometry thus forcing the network to regress more accurate poses.We also propose a variant called EpiSingle to cope with non-sequential training images, which can construct the epipolar geometry constraint based on a single image in a self-supervised manner.Extensive experiments on the public indoor 7Scenes and outdoor RobotCar datasets show that the proposed pixel-level constraint is valuable, and helps our EpiLoc achieve state-of-the-art results in the end-to-end camera localization task.
Hot Keyword Extraction of Sci-tech Periodicals Based on the Improved BERT Model

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure.Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords.In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.
Characterization of Spatio-Temporal Variability of Irradiation, Nebulosity and Aerosols Optical Depth in 10&amp;amp;#730; North-20&amp;amp;#730; North African Band

This work concerns the 10˚ North, 20˚ North African band.Area renowned for having some of the poorest countries in the world.It is also home to the Sahelian strip and part of the Sahara.The countries in this zone have a relatively low electrification rate compared to the enlightened country.To solve this problem, these countries want to turn to renewable energies such as photovoltaics (renewable energy obtained through solar radiation).Therefore, understanding the behavior of irradiation and cloudiness in the 10 -20 band becomes necessary.The application of the empirical orthogonal functions to the different cloud layers and to the irradiation reveals a seasonality of the latter, in particular compared to the first modes of the empirical orthogonal functions (EOF1).Indeed, by filtering in time and space to isolate solar radiation and cloudiness, the EOF1 expresses respectively 94.3% of the variation of descending solar radiation in clear sky in the 10 -20 band.Note 65.7% for global radiation, 54.4% for cloudiness at 450 hPa, 69.6% for cloudiness between 800 hPa and 450 hPa, 76.6% for low cloudiness, 61.4% for total cloudiness.These results allow us to say that we have generally good sunshine over several months, with little cloud cover in this band.However, since this zone is considered to be part of the main aerosol emission source zones, it is important in their temporal consideration of the optical depth.By doing the wavelet analysis on the optical depth data from Dakar and Banizoumbou, we note that the average dust presence spectrum over the entire period from
ANALYZING ADVANCED EXPERIENCE OF THE JUNIOR ACADEMY OF SCIENCES OF UKRAINE IN SCIENTIFIC EDUCATION

Integrating into the European Research Area, Ukrainian scientists try to bring local terms and concepts in line with the corresponding international ones, which is not always an easy task since western terminology cannot be translated literally without regard to local scientific tradition that has developed in specific historical conditions. In particular, a large national network of extracurricular research institutions, the Junior Academy of Sciences of Ukraine (JASU), has been operating for nearly a century in Ukraine. It is focused on the nurturing future generation of scientists and inventors, and has an unprecedented range of activities. When selecting an equivalent for an international concept, however, can we call such education a Science education if the JASU applies the scientific method in teaching not only natural sciences and interdisciplinary fields but also humanities and social subjects? And when studying the innovations of the organization, can we consider the use of a scientific method to be innovative, which has been underlying the JASU’s educational activities since its establishment? And at the same time application it in general educational institutions is innovative. During the research and identification of the JASU’s innovative educational practices, we had to address these issues and weigh all subtle aspects. The author presents the results of research into the innovative practices of scientific education where the latter is assigned a broader meaning than science education, taking into account all the disciplinary diversity of the JASU system. However the science education are deemed to be the basis for scientific education and a source of innovation activity enabling to respond to current challenges. The article defines the concepts of “scientific education”, “specialized science-oriented education”, “educational practices of teachers”, “innovative practices in scientific education”, describes the features of scientific education: modern educational trends within certain innovative lines are outlined and substantiated, the principles for identification of the JASU’Ss high-potential innovative practices in the context of scientific education are presented and exemplified.
SelectAug: A Data Augmentation Method for Distracted Driving Detection

With distracted driving becoming one of the main causes of traffic accidents, deep learning technology has been widely used in distracted driving detection, which achieves high accuracy when the training and test data are identically distributed. However, this assumption cannot correspond to the real-world situation. In case of a small sample size, we usually utilize open datasets as training dataset. Thus the training data distribution and test data distribution are different, which may induce accuracy plummets. Concentrating on the unforeseen data shifts encountered under different data distributions in distracted driving detection application, it is extremely desired to develop the detection technique with high robustness. In order to alleviate the issue about data shifts encountered under different data distributions, we propose an innovative method, SelectAug, to enhance images by applying the selected important features of the images. The experimental evaluations on the StateFarm dataset show that our method outperforms prior methods, demonstrating its efficacy in detecting distracted driving behaviors scenes. Furthermore, our method also improves generalization performance under different data distributions for distracted driving detection, which allows open datasets to be applied to real-world scenarios.
Research on Distributed Energy Network Transaction Model Based on Blockchain

Distributed energy is an important technology to promote the development of clean energy. However, the existing distributed energy network has some problems, such as small application scop, slow development speed and lack of trust. This article proposes a decentralized, credible and efficient distributed energy transaction model based on blockchain technology in the distributed energy network. The transaction model is automatically executed through the smart contract, which deployed on the blockchain network without the participation of a third party. This approach reduces the maintenance cost of the trading system. While improving the efficiency of energy transaction, the scope of energy supply is expanded, the loss of energy transmission is reduced, the data security is improved, and the transaction process is transparent.
RESEARCH POTENTIAL OF A STUDENT OF A MODERN UNIVERSITY

The modern system of higher education in Russia aims at the holistic development of a university student as an active subject of professional activity. The reform of the higher education system is becoming a consequence of innovative transformations in all spheres of society. The basic theoretical training of a university student no longer provides him with competitiveness in the labor market, so knowledge-oriented teaching methods become irrelevant. Research ac- tivity comes first for university students – obtaining universal educational skills, the ability to independently master new literature, the ability to rationally organize their work process in conditions of time shortage.
Codifying Composable Graphic Libraries.

The author describes research into the process of making images understandable to a computer as a system of images that can be combined and repurposed for multiple needs.
Integration of IoT and Big Data Technologies for Higher Education

The Internet of Things (IoT) and big data are predicted to provide a plethora of possibilities and obstacles for higher education. Several technologies that include cloud computing, ubiquitous learning, and 3D printing may enhance the way students are taught and learn. In addition, these technological advancements have led to both advancements and a higher quality of life in many industries, including education, health, agriculture, and security. To guarantee accuracy in the predictions for IoT and big data, one must account for the trustworthiness of this current reality. Big data and the Internet of Things (IoT) have emerged as technologies that will revolutionize the education industry by enabling the delivery of excellent training, teaching, and research. Although, problems have emerged related to the technology described. Using IoT and big data, this research provides a methodology for greater learning. The present study proposes that learning, IoT, and big data may be combined by interconnecting intelligent links. As a result, the student is better equipped to succeed in class with relevant and effective learning in context. Another similarity between the two systems mentioned above is that they both have the problems of security, transparency, and being susceptible to plenty of information gathering. Thus, this study focuses on IoT and big data’s interactions and possibilities in education, as well as their associated problems.
ONTOLOGY DESIGN AND DEVELOPMENT OF TRANSMISSION LINE IN INSPECTION ROBOT

The progress of robot technology is increasingly rapid with information technology and artificial intelligence development.First, the basic principle of the transmission line is analyzed.Then, the inspection robot's device design and transmission line analysis are conducted according to the transmission line characteristics.Moreover, the weight of the suspended robot is experimentally analyzed according to the different heights of the inspection robot under different voltages of the transmission line.The results show that using the power frequency voltage of the transmission line can suspend the robot with maximum weight, reducing the gap between the live part of the transmission line and the pole and tower components and reducing the operation and maintenance cost.For the linear tower and tension tower in a continuous transmission line, the maximum weight of the suspension robot allowed for the transmission line with corresponding voltage level shall be the small value of the linear tower and tension tower.For transmission line inspection robots with different heights, the smaller the robot's height is, the greater the robot's weight borne by the transmission line is.When robot's height in the transmission line is 0.5m, 1m, and 1.5m, respectively, the maximum weight of the robot borne by the 500kV transmission line is 125kg, 105kg, and 90kg, respectively.The minimum weight of the robot borne by 110kV and 220kV transmission lines is 60kg.This thesis has a certain reference in the design and development of transmission line inspection robots.
PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS BLOG BAGI MAHASISWA

As professionals, lecturers must be able to use and utilize media to support the learning process. Through the use of learning media, the presentation of the material becomes easier. Various types of learning media that can be used, one of which is a blog media. Apart from being free and not acknowledging space and time, blogs are a very efficient communication medium so that lecturers and students can interact such as asking questions, discussing and so on. In addition, blog-based learning media are also more flexible, lecture materials can be presented in attractive forms such as videos, pictures, and also PPT. Blog-based learning media is not only beneficial for students but also for the lecturers themselves. The blog is proof of his professionalism. Through blogs, lecturers can also indirectly improve their quality to be more creative, hone lecturers' skills in literacy, and can add insight.