Reviewerly/exHarmony huggingface dataset:
Adaptive Texture Filtering for Single-Domain Generalized Segmentation

Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.
Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning

Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets.
Identification and Tracking of Groups of People using Object Detection and Object Tracking

Object detection is one of the most important areas in the fields of Data Science and Computer Vision. In this paper, we present a novel approach to identifying and tracking groups of people, couples, and individuals in videos by using deep learning-based object detection and object tracking techniques along with a proposed grouping algorithm. In this approach, transfer learning is applied on YOLO v3 model for the detection of people in video frames, and Deep SORT is applied for tracking each detected person throughout the video. Results obtained from person detection and person tracking were used by the proposed grouping algorithm to identify and track groups, couples, and individuals who are appearing in input videos. Our proposed grouping algorithm is based on the proximity between each individual and the time duration that proximity is maintained for. It also considers how to identify and track groups, when people are moving within the groups. This approach was evaluated using CCTV videos captured from the restaurant domain and it was able to perform the group detection and tracking tasks successfully with a precision of 0.7083, recall of 0.7906 and F1 score of 0.7471.
Sentiment Analysis with Neural Models for Hungarian

Sentiment analysis is a powerful tool to gain insight into the emotional polarity of opinionated texts.Computerized applications can contribute to the establishment of nextgeneration models that can provide us with data of unprecedented quantity and quality.However, these models often require substantial amount of resources in order to meet the desired performance expectations.Therefore, numerous research efforts are targeted to achieve high-quality results while lowering the resource needs by improving the structure and function of the models used.From a cognitive perspective, it is important to understand the mental state of users when they engage in activities that potentially reflect their feelings and emotions.With the emergence of the widespread use of digital solutions, users post opinionated texts on social media, which can be used as a valuable source to detect their underlying sentiments.Therefore, these platforms offer an unparalleled opportunity to perform sentiment analysis.In recent years, natural language processing tasks, like sentiment analysis, can be solved with high performance, if a pre-trained language model is fine-tuned.Herein we present the first neural transformer-based sentiment analysis model for Hungarian, which achieved state-of-the-art performance.Several limitation factors can occur during fine-tuning, such as the lack of training corpora with appropriate size or the complete absence of usable training material.In our experiment, we use data augmentation methods, specifically machine translation and cross-lingual transfer, to increase the size of our training corpora.Here, we demonstrate our experimentation with 9 different language models.Our work provides evidence for the increased efficiency of the trained models if translation text is added to the training corpora.Furthermore, using the augmentation technique, we could further increase the performance of our models.Consequently, our findings represent an important milestone in the advancement of sentence-level and aspectbased sentiment analysis in the Hungarian language.
Keyword Network Analysis on Early Childhood Education Welfare Using Online Article

Objectives The objective of this study was to analyze the keyword network of early childhood education welfare in Korea and to develop strategies to improve the quality of early childhood education welfare based on the major issues related to this topic.&#x0D; Methods The study used online news articles to analyze the frequency, network structure, and community subgroups of keywords related to “early childhood education welfare” in Korea. Tools such as Textom, Ucinet 6.0, and Gephi 0.10.0 were used for the analysis.&#x0D; Results The study analyzed 15,349 collected items from 2001 to August 2022, and identified four community groups: “School and universal education welfare,” “Plan and implementation of early childhood education welfare,” “Early childhood education and finance,” and “Strengthening publicness.” The study found that the social issues surrounding early childhood education welfare in Korea are shifting towards universal welfare from selective welfare. The study identified the top keywords and their frequency, and noted that the current issues related to early childhood education welfare in Korea primarily focus on kindergartens managed by the Office of Education and lack diversity.&#x0D; Conclusions TTo improve early childhood education welfare in Korea, it is necessary to supplement the quality and quantity of education welfare with demand-oriented customized solutions.
Mediating effect of professional perception in the relationship between the teaching efficacy of childcare teachers and the performance of teacher roles

Objectives The purpose of this study is to examine the relationship between childcare teachers' teacher efficacy, teachers' professional perception, and role performance, and to examine the mediating effect of professional perception on teacher efficacy and role performance.&#x0D; Methods The study subjects used data collected from 212 nursery teachers from daycare centers located in Gyeonggi, Seoul. The structural equation model was analyzed to identify the structural relationships between the major variables, and the bootstrapping method was used to determine the mediating effect.&#x0D; Results First, the results of this study showed a significant static correlation between teacher efficacy, professionalism recognition, and teacher role performance of childcare teachers. Second, it was discovered that childcare teachers' perceptions of expertise had a direct impact on their ability to perform their roles. Third, there was a partial mediating effect of professionalism recognition in the relationship between teacher efficacy and role performance of childcare teachers.&#x0D; Conclusions It is important to raise teachers' awareness of efficacy and professionalism in order to increase the level of role performance of childcare teachers. Based on the results of this study, basic data were provided for the development of national policies and professional education programs be of significance to.
The Analysis of the Factors in E-Learning Environment Affecting on 2020 National Assessment of Education Achievement for English Subject among High School Students

Objectives The purposes of this study is to examine the effects of the factors in E-learning environment due to COVID19 on 2020 National Assessment of Education Achievement(NAEA) for English subject among high school students.&#x0D; Methods About 3 % of sampling classes were participated in NAEA. The achievement test results and the survey results of 10,526 2nd grade of high school students were used in the analysis except the missing values. The demographic characteristics(gender and regional scale) were served as the control variable and hierarchical regression analysis was used.&#x0D; Results The research results identifies the factors in E-learning environment affecting scholastic achievement of the English subject after controlling gender and regional scale. Model 1 explains approximately 3.6% and Model 2 shows about 11.9% of coefficient of determination respectably. Students who acknowledge the usefulness of E-learning have higher English scores. Also, students with higher English scores feel that E-learning environment is similar to that of school life. The variables such as ‘time used on private tutoring’, ‘time used on self-directed learning’, ‘anxiety on grade’, ‘interest in learning’ have positive correlation with academic achievement of the English subject. On the other hand, the variables such as ‘sense of belonging to the school’, ‘communication with the teachers regarding career plan’, ‘hangout time with friends’, ‘the amount of home work’, ‘the amount of study’, ‘communication with teachers regarding academic matters’, ‘life style on regular base’ have negative correlation with the academic achievement of the English subject.&#x0D; Conclusions The students with high level of English academic achievement tend to perceive that the amount of homework and that of learning are reduced in E-learning environment due to COVID19 compared to in-class learning environment. They feel more anxiety on grade, thus spend more time on tutoring and self-directed study in order to make up the lack of study. It is desirable that the results of current study help aware education gap caused by COVID19 and are able to served as a springboard to provide useful information to establish sound educational policy.
Research trend analysis of college students’ career preparation behavior using keyword network analysis

Objectives The purpose of this study were to analyze the trend of college students' career preparation behavior research from 1997 to 2022 through keyword network analysis.&#x0D; Methods Among the papers published in RISS, papers related to career preparation behavior of college students were extracted and divided into the first half (1997-2015) and the second half (2016-2022) based on 2016. Keyword frequency analysis and network analysis were performed and visualized for each period.&#x0D; Results As a result of the network analysis, the keywords with high connection centrality and mediation centrality in the first half were ‘career decision self-efficacy’, ‘self-efficacy’, ‘career maturity’, ‘career decision level’, ‘career barriers’, ‘career identity’, ‘employment preparation behavior’ and ‘self-leadership’. And in the second half year, ‘career decision self-efficacy’, ‘major satisfaction’, ‘career barrier’, ‘social support’, ‘career decision level’, ‘nursing college student’, ‘job preparation behavior’, ‘grit’, and ‘mediating effect’ were. ‘Career maturity’, ‘self-efficacy’, and ‘career decision’ were keywords with high centrality in the first half year, but lowered in the second half, while ‘major satisfaction’, ‘social support’, ‘nursing college student’, ‘grit’, ‘positive psychological capital’, ‘career adaptability’, ‘career elasticity’, and ‘resilience’ were keywords with low centrality in the first half, but increased centrality in the second half.&#x0D; Conclusions The trend of research on career preparation behavior of college students showed differentiated characteristics by period. Studies on environmental variables and the ability to cope with job insecurity and uncertainty in work increased in the second half.
A Study on Practical Classes and Teacher Professionalism Improvement for the Implementation of the 2022 Revised Art Curriculum Objectives for Elementary School

Objectives The purposes of this study were to analyze the objectives of revised art curriculum, and to propose improvements for elementary art education.&#x0D; Methods To achieve this, the 2015’s and 2022’s revised curriculums were compared and analyzed, and, previous studies related to the elementary art education plans were reviewed, and improvement plans were considered.&#x0D; Results The 2022 revised curriculum emphasizes the linkage between aesthetic experience, expression, and appreciation, compared to 2015’s. Additionally, it appears to emphasize the goals of art education from an integrated perspective. However, according to previous studies, there are limitations in the separation of domains and the development of teaching methods. In particular, the limitations of materials and expertise were pointed out in the expression class. As a result, improvement was proposed for practical classes and the cultivation of teacher expertise.&#x0D; Conclusions This study is significant in that it examined practical improvement for achieving the goals of elementary school revised art curriculum by comparing and analyzing their goals and limitations.
The significance and educational plan of U-hui as a subject of classical drama education

Objectives The purpose of this study is to analyze the current status of classical drama education in literature education and to clarify the significance of U-hui as a subject of classical drama education.&#x0D; Methods To this end, the content related to theater history presented as educational content in the literature curriculum was examined diachronically. In addition, the educational content related to theater history covered in current literature textbooks was analyzed by dividing them into explanatory materials, work materials, and learning activities in the textbook history section. Based on the structure of the revised educational goal classification system, a method for generating knowledge according to the development of the learner's cognitive process was presented.&#x0D; Results The influence of the syllabus and the period of the 4th curriculum on classical theater education leads to fixed knowledge, and in textbooks, academic discourse on theater history in the past is reproduced or knowledge that has formed an issue is avoided. Accordingly, the content of education was organized around the theater and commoners in the late Joseon Dynasty.&#x0D; Conclusions In order to solve this problem, the current academic discourse related to Woohee should be included, and the cognitive process in which learners evaluate knowledge and create new knowledge should be used as an educational method. Taking U-hui as a subject of classical drama education enables a change from the educational content centered on the common people in the late Joseon Dynasty to encompassing various eras and enjoyment classes. In addition, the change in teaching and learning methods that utilize research historical issues has significance in helping learners maintain an open attitude toward knowledge of the history of literature.
A Layered Encryption Model PABB Based on User Privacy in E-commerce Platforms

In the era of big data, e-commerce platforms have increasingly strict requirements for encryption systems to prevent user privacy breaches. Traditional encryption systems use a single encryption algorithm, which cannot achieve a balance between efficiency and security. The layered encryption model PABB classifies and layers user data of different security levels, using targeted different encryption algorithms for protection, balancing the requirements of security and efficiency.
A New Approach to Signal-to-Noise Ratio Estimation in Adaptive Doppler–Kalman Filter for Radar Systems

Doppler frequency carries the information about the relative velocity of the object regarding the radar antenna. However, since the target maneuvers can be temporary with high intensity, Doppler frequency has to be estimated by using a window function in the frame of time-dependent Fourier transform. In order to minimize the estimation error, the window function width should be adaptive. The window length adaptation has been performed based on the estimates of target acceleration and signal-to-noise ratio (SNR). In this paper, we give focus on the SNR estimation and propose a new approach based on autoregressive method of spectral estimation, where in one step the amplitude of the sinusoidal signal and noise variance are estimated. The simulation results justify the advantage of the proposed approach in general applications of signal processing of one sinusoid in white noise environment.
Semiparametric Estimation of Distribution Algorithms for Continuous Optimization

Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to optimize continuous functions, such as the estimation of Gaussian network algorithms (EGNAs) which use Gaussian Bayesian networks (GBNs). However, this assumes a parametric density function, and, in GBNs, linear dependencies between variables. Furthermore, the EGNA baseline learns a GBN at each iteration based on the best individuals in the last iteration, which may lead to local optimum convergence or large variance between solutions across multiple independent runs of the algorithm. In this work we propose a semiparametric EDA in which the restriction of assuming Gaussianity in the variables is relaxed using semiparametric Bayesian networks, in which nodes estimated by kernels coexist with nodes that assume Gaussianity, and the algorithm itself is able to determine where to use each type of node. Additionally, our approach takes into account information from several past iterations to learn the semiparametric Bayesian network from which the new solutions are sampled in each iteration. The empirical results show that semiparametric EDAs are a useful tool for continuous scenarios compared to different kinds of EDAs and other optimization techniques in continuous environments.
An Energy-Based Prior for Generative Saliency

We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Both the parameters of saliency generator and the energy-based prior are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Different from existing generative models, which define the prior distribution of the latent variables as a simple isotropic Gaussian distribution, our model uses an energy-based informative prior which can be more expressive in capturing the latent space of the data. With the informative energy-based prior, we extend the Gaussian distribution assumption of generative models to achieve a more representative distribution of the latent space, leading to more reliable uncertainty estimation. We apply the proposed frameworks to both RGB and RGB-D salient object detection tasks with both transformer and convolutional neural network backbones. We further propose an adversarial learning algorithm and a variational inference algorithm as alternatives to train the proposed generative framework. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps that are consistent with human perception. Results and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JingZhang617/EBMGSOD</uri> .
Development of Teaching Material for Procedural Text through Nearpod Media for XI Grade Senior High School Students

This research aims to develop teaching materials for procedure text through Nearpod media. The research was prepared using the Research and Development (R&amp;D) method with the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). Data collection was carried out through needs analysis interviews to teachers and distributing questionnaires to students through g-forms, curriculum analysis, teaching material analysis, material expert validation and media expert validation. In addition, the feasibility test was conducted to Indonesian language teachers and students of grade XI high school. Data analysis was carried out with qualitative and quantitative descriptive data analysis. The results of this study are in the form of teaching materials for procedural texts that can be accessed through Nearpod media. The feasibility of the product of teaching material for procedure text through Nearpod media for grade XI high school is included in the very good category. The results of the material expert assessment obtained an average score of 3.82. Furthermore, the media expert assessment obtained an average score of 4.5. The Indonesian language teacher assessment obtained an average score of 4.42. The student assessment obtained an average score of 4.28. Based on the results of this assessment, the teaching material for procedural text through Nearpod media for grade XI high school students is declared suitable for use in the learning process.
Thinking about the Way of English Pronunciation Training Penetrating into English Speech Training

English speech is the main teaching auxiliary means of English pronunciation teaching, which not only improves students' English speech ability, but also improves students' professional skills and vocational skills, conforms to the educational concept of modern vocational education system, and plays a positive role in the integration and development of skills.The relationship between English speech and English phonetic teaching is close.Integrating English speech into English phonetic classroom teaching can effectively improve the classroom teaching effect of pronunciation, help students master English phonetic knowledge, and expand teachers' evaluation mechanism on the teaching effect.