Reviewerly/exHarmony huggingface dataset:
Construction of Classroom Dynamic Evaluation Model based on Internet of Things

With the development of computer technology and Internet technology, the current information exchange processing and analysis sharing has entered the era of big data.In the new era, the society puts forward higher requirements for students' English learning.As the main standard to test the quality of higher education, students' foreign language ability should not only stay in the stage of knowledge acquisition, but also be reflected in practice and service.Therefore, it is necessary to construct a set of perfect teaching dynamic evaluation model for College English teaching, and promote the improvement of teaching quality and level with the help of the evaluation function of the model.
An Investigation on Impact of Gender in Image-Based Kinship Verification

The task of kinship verification is to establish a blood relationship between two persons. Kinship verification using facial images provides an affordable solution as compared to biological methods. KV has many applications like image annotation, child adoption, family tree creation, photo album management, etc. However, the facial image verification process is challenging because images do not have fixed parameters like resolution, background, age, gender, etc. Many parameters are affecting the accuracy of the methods. One such parameter is the gender difference in the kin relation. We have investigated the impact of the gender difference in the kin relation on popular methods available in the literature. The investigation suggests that gender difference affects kin detection accuracy.
Network attack evaluation model based on directed weighted complex network and fuzzy AHP method

At present, most of the network attack effect evaluation is based on mathematical theory. The calculation process is influenced by artificial judgment, and the research on the dependence and influence between indicators is not sufficient, so the index weight setting is too subjective. This paper proposes a network attack effect evaluation model based on directed weighted complex network, and constructs an index architecture based on attribute-attack type-atomic function-evaluation index four-layers subnetwork. The index importance calculation method is given by combining the complex network theory with the traditional fuzzy analytic hierarchy process. Finally, taking the password cracking attack as an example, it is proved that this method is helpful to explore the impact mechanism of indicators on attributes, fully considers the relationship between indicators, simplifies the index system and improves the evaluation efficiency.
Toward Machine Learning and IoT Environment-Enabled Smart Personal Health Monitoring Framework

The healthcare industry is mostly “information rich;” however, sadly, not all the information square measure mined that is needed for locating hidden patterns and effective higher cognitive process. Progressed information handling methods square measure is used to find information in data and for clinical examination, fundamentally in heart condition forecast. This paper has investigated forecast structures for heart condition abuse a ton of assortment of enters credits. The contraption utilizes logical terms like sex, circulatory strain, and ideal cholesterol like thirteen credits to expect the likelihood of patient getting a heart ailment. The insights mining class procedures, in particular, multi-layer perceptron (MLP), random forest, support vector (Linear), Naïve Bayes, and AdaBoost are broke down on cardio-vascular disease dataset. The general exhibition of these methods is as analyzed, in light of precision. As in accordance with our results, precision of multi-layer perceptron (MLP), random forest, support vector (Linear), Naïve Bayes, and AdaBoost are 86.88%, ninety.16%, eighty three.60%, eighty five.24%, and ninety.16%, individually. Our assessment recommends that out of these three class models, neural networks predict heart condition with most extreme exactness.
The Influence of the Langkat Smart Community Movement on Increasing Children's Interest in Reading in Langkat Regency

This study aims to determine the influence of the Gerakan Langkat Pintar Community in increasing children's interest in reading in Kwala Bingai, Langkat Regency by using a descriptive quantitative approach.The sample in this study were 30 children.The data collection tool used is a questionnaire, with data analysis techniques using simple linear regression and hypothesis testing.The findings of this study based on data processing with simple linear regression obtained linear regression equation is = 51.778+ 0.744x, with the product moment correlation rxy = 0.744.rcount > rtable is 0.744 > 0.306 and the result of tcount > ttable is 5.891 > 1.697 then the hypothesis is accepted.So, it can be concluded that there is "The Influence of the Gerakan Langkat Pintar Community in Increasing Children's Reading Interest in Kwala Bingai, Langkat Regency.
A Model of Forming Sustainable Small Research Group in Higher Education

In carrying out research tasks, universities need research groups that function in a sustainable manner.In many cases, research group members change frequently, to a large extent due to incoming and outgoing students.This study aims to describe a model of forming a small research group (4-6 lecturers, 3-6 fulltime students), which has been sustainable for 13 years.The focus of this research is the process that takes place in the last 4 years, with communications mainly via online and social media, which is the norm during the covid-19 pandemic.The study was conducted using a participatory ethnographic approach.The participants were four lecturers and six students.The dynamic process in the research group could be explained by the theory of the small research group as a complex system (Arrow, McGrath, and Berdahl, 2000).The results of the study showed that the creation process starts from initiation and induction, trust building, natural selection, heedful interrelating, and mindfulness comprehension with collective identity.Through these processes the group becomes able to achieve productive sustainable research outcomes.This study contributes a model of the creation of a sustainable research group.
Solving Reality-Based Trajectory Optimization Problems with Metaheuristic Algorithms Inspired by Metaphors

In recent years, optimization problems based on real-world situations have become increasingly complex. Designers have endless sources of inspiration to create new and better methods that are then improved, adapted, and combined to enhance their performance. However, rarely do we see the original proposals’ performance with the most complex problems. In the present work, we chose a group of algorithms in their original version to solve a particular set of problems. Knowing the performance of the base algorithm is the first step to knowing what is required to improve its results when optimizing complex problems. The problems used are known as Global Trajectory Optimization Problems (GTOP). The results were validated with statistical tests that present two swarm intelligence algorithms and one mathematical metaphor algorithm as the three methods in their original version with the best performance when solving complex problems.
Development of Android-Based Educational Games to Improve Letter Recegnition Ability in Early Childhood in Kinali Pasaman Barat

Based on the preliminary study that the researcher did, the researcher saw that there were still many children who did not know the initial letters in terms of recognizing symbols such as the letters of the alphabet.This study aims to produce educational games to improve early childhood letter recognition skills that are valid, practical, and effective.This research uses the Branch ADDIE model development method.which stands for Analysis, Design, Development, Implementation and Evaluation.Data collection techniques used in the form of a questionnaire.The results obtained from the development of children's reading skills through android-based educational games were declared valid, practical and effective.In the initial development process, the developer consults with media experts to get input and suggestions about the developed media.To determine the validity of the product, expert validation of media experts, material expert validation and field expert validation tests (expert lecturers) were carried out to determine the validity of the product.The results of the assessment were analyzed using a Likert scale.Informants in this study were teachers and children.Data collection techniques used consisted of questionnaires, and documentation.The data analysis technique uses validity analysis, practicality analysis and effectiveness analysis.
M-Learning in Serbian Secondary Education – a Covid-19 Side Effect

Learning represents a relatively permanent and progressive character of an individual's personality and behaviour change, and it arises as a product, i.e., the result of a specific experience.Modern digital technologies have further contributed to the improvement of learning.Some of these technologies have been developed specifically for educational purposes, but most had to be adapted to be suitable for teaching and/or learning.These communication and mobile technologies became crucial during the Covid-19 pandemic.The goal of this paper is to determine how current mobile technologies and devices can further be applied in secondary education and to analyse student habits and attitudes towards using mobile devices in secondary education in the light of returning Serbian secondary schools to traditional classes after a two-year-long state of a medical emergency.
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity

Ensuring relevance quality in product search is a critical task as it impacts the customer's ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term.In this work we leverage a high-precision crossencoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization.We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.
A Study on China’s TESOL Pre-Service Teachers’ Knowledge and Attitudes on Gamification

In recent years, educational gamification is getting more attention together with the fast advancing of technology as one efficient way to boost learning outcome.Incorporating gamification in learning has an overall better outcome in many aspects.With the popularity of digital gaming, the potential of digital game-based language learning has also been an increasing focus of educational research.Although the reluctance of EFL in-teachers to adopt digital technology has already been explored, the perspectives/perceptions and experiences of EFL pre-service teachers on using gamification in EFL classrooms remain relatively unknown.thus, the focus of this paper was to explore EFL pre-service teachers' experiences, perceptions, and levels of teacher leadership on digital game-based language learning /gamification.The results of this study found that most pre-service teachers have a positive attitude and high level of teacher leadership toward using gamification in their future classrooms.However, this study also yielded some worrisome results.This study has implications on future teacher education program development on integrating gamifications.
Legendre Polynomial Kernel: Application in SVM

In machines learning problems, Support Vector Machine is a method of classification.For non-linearly separable data, kernel functions are a basic ingredient in the SVM technic.In this paper, we briefly recall some useful results on decomposition of RKHS.Based on orthogonal polynomial theory and Mercer theorem, we construct the high power Legendre polynomial kernel on the cube [ ]. Following presentation of the theoretical background of SVM, we evaluate the performance of this kernel on some illustrative examples in comparison with Rbf, linear and polynomial kernels.
Implementation of BOS Reporting Information System Management at SMP Negeri 2 Medang Deras, Batubara Regency

Management of information systems is a must in all organizations, both nonprofit organizations and profit companies.The application of information technology has become a very important part, especially reporting on BOS funds at SMP Negeri 1 Medang Deras, Batu Bara Regency.In 2020 until now, the process of distributing BOS Funds from the Ministry of Education and Culture has been different from the previous year.Where the distribution of BOS funds before 2020, the distribution is done every three months, but in 2020 until now the distribution of BOS funds has changed into 3 stages, namely stage one, stage two and stage three.For this reason, the presentation of financial reports at SMP Negeri 2 Medang Deras, Batu Bara Regency is an important aspect because the reporting must be fast, transparent and according to the specified time.At the time of reporting, recording problems often arise so that reports are late to the Coal Education Office and to the Ministry of Education and Culture because they use the Microsoft Excel application.To overcome this problem, a BOS Reporting System Application was developed at SMP Negeri 2 Medang Deras, Batu Bara Regency.The BOS Reporting System application can be used starting from RKAS Preparation, General Cash Book Financial Reporting, Bank Books, Tax Books, Cash Support Books and Asset and Inventory Reporting.
Debiasing Event Understanding for Visual Commonsense Tasks

We study event understanding as a critical step towards visual commonsense tasks.Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects.We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction.We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.
REFLECTION OF THE AUTHOR’S PUBLICATIONS AND STU-DENTS’ ARTICLES ON THE PAGES OF THE JOURNAL «BIBLIOPANORAMA» (2009-2020)

Within the complex study of the department of library and information resources «Book culture of the Baikal region», library communication and eth-ics were one of the directions of library science of the Republic of Buryatia within the period of 1991-2021. Various ways of its research have been re-flected in the journal «Bibliopanorama» (2009-2020) for 11 years, which was published by the National library of the Republic of Buryatia (executive editor R.I. Khamaganova). The article is devoted to the analysis of the development of this problem, in which the author of the publication reflected her experience and that of some bachelors and magistrates whose graduation projects she had supervised.
Generation of Walking Sensation by Providing Upper Limb Motion

For an immersive first-person experience in a VR space, the user (or an avatar)needs to walk freely within the space because locomotion is essential and the basisfor a variety of activities. The present study investigates the arm swing of a user toinduce a bodily sensation during walking in a VR space. We built an arm swingdisplay that moves the upper limbs similarly to the motion appears in a real walking.The amplitude and flexion ratio of swing motion to present the sensation of virtualwalking were determined via the user study. The optimal swing angle and itsforward part ratio (flexion ratio) to create the sensation of a straight waking werearound 30 degrees and 54 percent, respectively. For a turning walking, the relationbetween the asymmetric flexion ratio of both arms and the curvature of a turningpath was also determined by an experiment.
AN ADAPTIVE PSEUDOGRADIENT ALGORITHM TO DETECT SIGNALS AGAINST BACKGROUND OF QUASIPERIODIC PROCESSES

Some parameters of many objects in practical situations are quasi-periodic, or there is a noticeable repeatability with random variations. Such parameters can be represented by random quasiperiodic processes, for example speech signals, aircraft engine noise and vibration, etc. From time to time, against the background of this process, special structures (signals) may appear that arise due to the influence of internal or external factors on the object, for example, impacts of external objects. In this article an autoregressive model is used to represent quasi-periodic processes. It is assumed that the detected signals interact additively with the background (process) and have a known shape. The detection algorithm is based on the classical likelihood ratio. In this case, the statistics of the decision rule is represented through the compensation of the background ‘to the point’, that is, through the subtraction from the observation at the point of its forecast for all other observations. The parameters of the background model may be unknown; therefore, an adaptive pseudogradient algorithm for optimizing the parameters of the background compensator is applied. In the case of homogeneous processes, the compensator converges to the optimal one, constructed with known background parameters, and in the case of inhomogeneity, compensation and detection are carried out that are close to optimal. The results of statistical tests of the developed algorithm are presented.
Grayscale Image Histogram Estimation Structure Based on FPGA

In recent years, most of the research work is done statistical image description as histogram estimation. In this we proposed a PDF architecture used to estimate the histogram of a grayscale image of 16 samples. Because of effective hardware utilization of histogram structures, it is well suited to generate histograms for various applications like military and medical applications which is not possible in MATLAB software. The histogram structures are implemented in FPGA platform. These histogram estimation values generated by the modelsim are similar to the histogram estimation values of MATLAB output with 100% accuracy. Experimental results shows that slice registers, slice look up tables (LUT) and frequency of proposed structure is improved when compared with the existing structure for all the sample values.
A Novel Robust Adaptive Color Image Watermarking Scheme Based on Artificial Bee Colony

Abstract This paper proposes a new robust adaptive watermarking scheme based on dual-tree quaternion wavelet and artificial bee colony, wherein the host images and watermark images are both color images. Color host images and watermark images in RGB space are transformed into YCbCr space. Then, apply Arnold chaotic map on their luminance components and use the artificial bee colony optimization algorithm to generate embedding watermark strength factor. Dual-tree quaternion wavelet transform is performed on the luminance component of the scrambled host image. Apply singular value decomposition on its low-frequency amplitude sub-band to obtain the principal component (PC). Embed the watermark into the principal component. Analysis and experimental results show that the proposed scheme is better as compared to the RDWT-SVD scheme and the QWT-DCT scheme.
ARGUMENTATIONOF THE VISUAL COMPONENT OF INFORMATION EDUCATIONAL RESOURCES OF HIGHER EDUCATION

ПЕДАГОГИЧЕСКИЕ НАУКИ( 5.8.1, 5.8.1 (13.00.05), 5.8.2, 5.8.3, 5.8.4 (13.00.04), 5.8.5 (13.00.04), 5.8.6 (13.00.04), 5.8.7)УДК 378.
IISERB@LT-EDI-ACL2022: A Bag of Words and Document Embeddings Based Framework to Identify Severity of Depression Over Social Media

The DepSign-LT-EDI-ACL2022 shared task focuses on early prediction of severity of depression over social media posts.The BioNLP group at Department of Data Science and Engineering in Indian Institute of Science Education and Research Bhopal (IISERB) has participated in this challenge and submitted three runs based on three different text mining models.The severity of depression were categorized into three classes, viz., no depression, moderate, and severe and the data to build models were released as part of this shared task.The objective of this work is to identify relevant features from the given social media texts for effective text classification.As part of our investigation, we explored features derived from text data using document embeddings technique and simple bag of words model following different weighting schemes.Subsequently, adaptive boosting, logistic regression, random forest and support vector machine (SVM) classifiers were used to identify the scale of depression from the given texts.The experimental analysis on the given validation data show that the SVM classifier using the bag of words model following term frequency and inverse document frequency weighting scheme outperforms the other models for identifying depression.However, this framework could not achieve a place among the top ten runs of the shared task.This paper describes the potential of the proposed framework as well as the possible reasons behind mediocre performance on the given data.
Improving Social Engineering Awareness, Training and Education (SEATE) using a Behavioral Change Model

Social Engineering (SE) Awareness, Training, and Education (SEATE) is one of the recommended defenses against SE attacks among users of Information Systems.However, many of these SEATE programs fails to achieve the desired impact leading to exposures.This study sought to explore SEATE programs to identify gaps/challenges and propose relevant content, Delivery Methods, and a novel behavioral change Model to improve SEATE programs among users.An explorative Literature Search was conducted on the relevant SEATE Content, Delivery methods and the challenges of SEATE Programs.Consequently, the relevant and critical content and delivery methods were proposed.The challenges that impede the efficient and effective conduct of SEATE Programs were established.A behavioral change Model known as Social Engineering Awareness, Transition, Adaptation and Consolidation (ATAC) based on Stable-Quasi-Stationary Equilibrium theory was proposed.The model was validated using Expert Opinions.Five (5) expert in cybersecurity were recruited to appraise the model based on five metrics; fit for purpose, novelty, ease of use and structure.The results show that, challenges still exist in the conduct of SEATE programs.To improve SEATE programs requires relevant and innovative content, and delivery method (Hybrid Approach).Validation of the proposed behavioral change model showed an average score at 73.6% and performance metrics at 92%.As the menace of SE attacks rages on and exploiting the user, the need for SEATE programs remains imperative.A well-developed and relevant content, delivery methods and a clear understanding of the challenges is required to improve SEATE.Following the model developed, and the repeated use of it will lead to improving user resistance and or immunity to SE attacks and by extension improve security culture among users.