Reviewerly/exHarmony huggingface dataset:
User Interface Modeling for Basic English Learning Applications using the Child-Centered Design Method

Interactive media convey information through videos or images that can move with computer control in their presentation. Interactive media have a positive effect on children's language development. A language is a tool that humans need to communicate with each other. With the development of technology, especially in communication, Indonesian people have begun to learn English which is used as a means of communication between nations around the world. According to the English Proficiency Index, Indonesia will be ranked 80th out of 112 countries in the world in 2021. This shows that Indonesia is included in the low ability group. One of the efforts to improve English language skills is to introduce basic material from an early age. Children aged 0-6 years quickly absorb and receive various information. This study aims to build a UI modeling of basic English learning applications according to the needs and criteria of kindergarten-age children, which was built using the Child-Centered Design method to analyze the tasks required by the user and evaluate the level of usability using the USE Questionnaire method with a Likert Scale of 1-5. The findings of this study provide user interface design recommendations for basic English learning applications and have a usability score of 88.75. These findings indicate that this application is easy to use and provides a positive experience in learning basic English for kindergarten-age children.
Data Mining Tools for Generate Item Set : Critical Review

Most algorithms used to identify large itemsets can be classified as either sequential or parallel. In most cases, it is assumed that the itemsets are identified and stored in lexicographic order (based on item name). This ordering provides a logical manner in which itemsets can be generated and counted. This is the normal approach with sequential algorithms. On the other hand, parallel algorithms focus on how to parallelize the task of finding large itemsets. Mining Associations is one of the techniques involved in the process mentioned in chapter 1 and among the data mining problems it might be the most studied ones. Discovering association rules is at the heart of data mining. Mining for association rules between items in large database of sales transactions has been recognized as an important area of database research. These rules can be effectively used to uncover unknown relationships, producing results that can provide a basis for forecasting and decision making. Today, research work on association rules is motivated by an extensive range of application areas, such as banking, manufacturing, health care, and telecommunications. It is also used for building statistical thesaurus from the text databases, finding web access patterns from web log files, and also discovering associated images from huge sized image databases.
ANALYSIS OF THE EFFECTIVENESS OF TALKING COMPUTERS AS A LEARNING MEDIA FOR BLIND STUDENTS

This article aims to evaluate the effectiveness of teaching visually impaired pupils using talking computers; and compare the learning experiences of students before and after incorporating such technology into their education.Every person in the study was a blind student at a college.This research used the qualitative approach for description.According to the results of this study, it is possible to conclude that talking computers first appeared around 2000 and remain popular to this day.Unfinished software, a high learning curve for the keyboard and symbols, and a shortage of qualified computer voice synthesizers work against the device's promise and hold it back from reaching its full potential.Student degrees of enthusiasm might vary anywhere from normal to extraordinary.The curriculum will be introduced in stages, depending on each student's current skill level.We believe that students' ability to make effective use of computers in the learning process will continue to improve because of the increasing number of interventions that are becoming accessible.
USE OF ADAPTIVE LEARNING MEDIA FOR STUDENTS WITH SPECIAL NEEDS

This research aimed to determine whether students with specific needs may benefit from using adaptive learning material.This study consisted of descriptive qualitative research conducted using a descriptive research technique.Participants in this study were school administrators, general education teachers, and general education teachers for kids with special needs.The data collection for this specific study used various methodologies, including interviews, observations, and documentation studies.Data analysis included, among other things, paring down the quantity of collected data, presenting the facts, and drawing conclusions.According to the study results, unique forms of adaptive learning tools are available to students who do not have any requirements to fulfill.There is no differentiation between students with special needs and other students when it comes to the use of learning media since these resources are general, and all students access them.
National Journal Articles Writing for Teachers of Early Childhood Education (PAUD) In Tegal City

Writing is one indicator for a teacher to be professional. Unfortunately, not all teachers are involved in this activity. It is better if PAUD teachers make writing activities necessary and obligated to produce their desires and thoughts. PAUD teachers are not only required to like to write articles but also actively produce written works that contain creative ideas or thoughts. The teacher will get several benefits from writing, including it can relieve stress, as a memory storage tool, a means of solving problems, and train teachers to think in an orderly and orderly manner. Therefore, it is essential and urgent to conduct training in writing scientific articles for journals for PAUD teachers. Through this activity, it hopes that teachers will be motivated to develop scientific works in the form of scientific articles published in scientific journals. In addition, institutions that serve scientific articles from activity participants can publish them in existing journals.
Compact Sparse R-CNN: Speeding up sparse R-CNN by reducing iterative detection heads and simplifying feature pyramid network

Processing a large number of proposals usually takes a significant proportion of inference time in two-stage object detection methods. Sparse regions with CNN features (Sparse R-CNN) was proposed using a small number of learnable proposals to replace the proposals derived from anchors. To decrease the missing rate, Sparse R-CNN uses six iterative detection heads to gradually regress the detection boxes to the corresponding objects, which hence increases the inference time. To reduce the number of iterative heads, we propose the iterative Hungarian assigner that encourages Sparse R-CNN to generate multiple proposals for each object at the inference stage. This decreases the missing rate when the number of iterative heads is small. As a result, Sparse R-CNN using the proposed assigner needs fewer iterative heads but gives higher detection accuracy. Also, we observe that the multi-layer outputs of the feature pyramid network contribute little to Sparse R-CNN and propose using a single-layer output neck to replace it. The single-layer output neck further improves the inference speed of Sparse R-CNN without the cost of detection accuracy. Experimental results show that the proposed iterative Hungarian assigner together with the single-layer output neck improves Sparse R-CNN by 2.5 AP50 on the Microsoft common objects in context (MS-COCO) dataset and improves Sparse R-CNN by 3.0 AP50 on the PASCAL visual object classes (VOC) dataset while decreasing 30% floating point operations (FLOPs).
Perception of Hate Speech by the Public and Experts: Insights into Predictors of the Perceived Hate Speech Towards Migrants

Online hate speech is a matter of concern for social media platforms, regulators, researchers, and the public. Despite its widespread prevalence and contentious nature, little research has been done on the perception of hate speech and its psychosocial predictors. To address this gap, we conducted a study on the perception of hate speech toward migrants in online comments, analyzing the differences between a public group (NPublic = 649) and an expert group (NExperts = 27) and exploring the correlation between the proposed hate speech indicators and perceived hate speech in both groups. Additionally, we explored various predictors of hate speech perception, including demographic and psychological variables such as human values, prejudice, aggression, impulsiveness, social media behavior, attitudes toward migrants and migration, and trust in institutions. Our results show that the public and experts have differing sensitivities toward hate speech, with the expert group perceiving comments as more hateful and emotionally harmful compared with the general population, who tend to agree more with antimigrant hateful comments. The proposed hate speech indicators and especially their total scores have a strong correlation with both groups' perceptions of hate speech. Psychological predictors, such as the human values of universalism, tradition, security, and subjective social distance, were significant predictors of online hate speech sensitivity. Our findings emphasize the need for public and scholarly discussions, more robust educational policies, and intervention programs with specific measures to counter hate speech online.
Lossless Compression of Large Aperture Static Imaging Spectrometer Data

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.
Pengaruh Metode Kerja Kelompok Pada Pembelajaran Matematika Terhadap Hasil Belajar Siswa Sekolah Dasar

This study aims to see the effectiveness of learning mathematics using the group work method. This is included in the quantitative descriptive research. This research uses a one group pretest posttest design. The research sample is 30 students of SDN 2 Kalibuntu. Data collection is in the form of observational notes regarding observations on student learning activities during learning activities and the teacher's way of managing learning, questionnaires containing student opinions, and test sheets to see student learning outcomes. Analysis of research data using correlation test and t-test techniques. This research shows that teachers get a very good category in managing learning, student activity shows an average of 100%, students get learning outcomes with completeness reaching 75%, and students' positive responses regarding group work methods are 95%. So learning mathematics by applying the group work method is effective in improving student learning outcomes in grades IV-VI at SDN 2 Kalibuntu.
Investigation and application of key technologies of aggregated flash payment based on marketing blockchain in the context of massive distributed generation grid connection

Abstract Optical flash payment technology is a form of optical flash payment application. Its payment scenario is mainly to directly realize “card free payment” in the consumption process through mobile devices, Bluetooth and other connection methods on mobile terminal devices such as smart phones under the mobile Internet environment. The “flash payment” is a more convenient payment method and a new payment channel without the function of electronic wallet. Compared with the current mainstream payment methods, it is more collaborative and complementary. Aggregated payment is a fourth party payment platform, which is different from Alipay, WeChat and other third payment platforms. Aggregated payment is a combination of technology and a platform that combines multiple payment methods. Based on the background of massive distributed power generation connected to the grid, this paper studied the key technologies of flash payment in combination with marketing blockchain, and discussed the data sharing mechanism of marketing information in multiple scenarios from the perspective of marketing blockchain by studying the data sharing mechanism in different scenarios. In this paper, a distributed optical flash payment system and a blockchain based encryption algorithm were proposed. Based on this research, the experimental results of key technologies of aggregated optical flash payment based on marketing blockchain were analyzed. The experimental results in this paper showed that 38.7 % of the users contacted the aggregation payment through the online platform. 11.7 % of people communicated through chatting with friends, and 9.8 % of enterprises got in touch through preferential policies. 4.5 % of them publicized through the media, and 8.3 % made requests through payment plans. 21 % of them transferred money through remittance, and 6 % of them got it through other ways. When using aggregate payment, the main reasons for users to choose aggregate payment were the network, friends chatting, and the acceptance of other people’s remittances. In the case that users used aggregate payment services, they most often used transfer and remittance, and most of these purchases were paid through mobile payment and credit card. In terms of users’ use of third-party services, it could be seen that transportation services and movie tickets were the most common ways of use. In terms of the aggregation payment strategy, the improvement of the three levels of aggregation payment could improve the risk prevention ability. First, the complaint mechanism was established to detect and correct potential vulnerabilities in the system in a timely manner. Second, consumers’ sense of self-protection should be strengthened. Third, the legal construction of the payment industry should be promoted to increase the strike force against illegal use.
E-LEARNING AS A FACTOR IN STIMULATING GIFTED STUDENTS TO ACHIEVE ACADEMIC SUCCESS

The aim of the study is to examine how gifted students are stimulated to use e-learning for academic achievement. The main questions to which an answer is sought in this context are: What types of activities do gifted students use in e-learning? How do students evaluate the adequacy of ICT resources for their academic achievements so far? What are the relationships between the use of ICT for learning and the personality traits, self-esteem, and motivational strategies of gifted people? The theoretical context consists of concepts of self-regulation, new concepts of intelligence - self-management of intellectual processes - Sternberg, emancipatory didactics. The research has used the method of systematic, non-experimental observation, and as instruments, a battery of questionnaires: (PSIKT) Protocol on students' self-perception about the importance and use of ICT and e-learning to stimulate students towards academic achievements—made for this research; Questionnaire of Motivation for Learning (LLOS-IEA), SMS-Scale of Motivational Strategies, Inventory of Competencies for Memory and Reasoning (MARCI-Stankov &amp; Crawford), and Personality Traits (BFIIV). of the battery ranges from 0.62 to 0.83. It was used as a 11 convenience sample, consisting of 687 students from the universities of Belgrade, Novi Sad and Niš (within this number, 127 are academically gifted, with an average grade above 9.00). Basic findings: When compared to other students, academically gifted students focus more on expanding their knowledge based on their interests, research work, and interactive self-evaluation tests in e-learning. Using ICT for learning and academic success is closely related to students' personal characteristics (personality traits, self-confidence, motivation). Gifted students have stronger self-confidence, motivation, and personality traits than other students.
ANT COLONY OPTIMIZATION TECHNIQUE FOR EDGE DETECTION OF AN IMAGE

Ant colony optimization (ACO) is a method used to address the problem of detecting edges in images, which is crucial for understanding their content.ACO works by mimicking the food foraging behavior of ant colonies, where positive feedback through pheromone updates helps ants discover good paths.The algorithm combines existing knowledge of the solution structure with information from previously successful solutions.ACO has been successfully used to extract edges from digital images, as demonstrated by experimental results.
CLASSIFICATION AND ANALYSIS OF MEDICAL IMAGES

Machine learning has phenomenal applications in the field of medicine.Image processing and analysis play a vital role in defining certain aspects of medicine that can save critical time in determining the patient's life.To develop and render a model to classify, visualize and analyze medical images using Python and TensorFlow.Machine learning has a vast array of applications, particularly in the field of medicine.This project works on the basis of Python and TensorFlow and numerous other libraries to classify, analyze and visualize the images and to evaluate the accuracy of prediction while doing so.This classification and analysis pose several applications to a medical professional as it helps save crucial time in the patient's life.The dataset involves slices of CT scans and X-Rays.The CT scan images are in the form of DICOM images extracted from TCIA (The Cancer Imaging Archive).The X-Rays are JPEG images of a person's chest extracted from Mendeley Data.The CT scans are visualized and analyzed to obtain a Segmentation 3D visualization from a 2D DICOM image.The chest X-Rays are visualized, analyzed, and classified as Pneumonia or Normal, which poses a task for a medical professional's eyes and is extremely difficult to catch.
Transmission-based noise spectroscopy for quadratic qubit-resonator interactions

We develop a theory describing the transient transmission through noisy qubit-resonator systems with quadratic interactions as are found in superconducting and nanomechanical resonators coupled to solid-state qubits. After generalizing the quantum Langevin equations to arbitrary qubit-resonator couplings, we show that only the cases of linear and quadratic couplings allow for an analytical treatment within standard input-output theory. Focussing for the first time on quadratic couplings and allowing for arbitrary initial qubit coherences, it is shown that noise characteristics can be extracted from input-output measurements by recording both the averaged fluctuations in the transmission probability and the averaged phase. Our results represent an extension to the field of transmission-based noise spectroscopy with immediate practical applications.
Evaluation of Sustainable Digital Agriculture Marketing Using The AHP Method

The increasing world population is estimated to reach about 9.7 billion people by 2050.In this case, intensive good agricultural practices aimed at increasing productivity in agricultural production activities were targeted in order to reach sufficient basic food sources.Agricultural businesses that follow a sustainable agricultural policy will benefit greatly in achieving the level of economic, social and environmental sustainability in the future by using their resources effectively and efficiently.With the increase in yield techniques and good agricultural practices depending on the developments in agricultural technologies, the issue of digitalization will play an important role in the agriculture and marketing sectors.Efforts to solve the problems related to the agricultural sector will make important contributions in the field of sustainable digital agriculture and marketing.In this study, the alternatives related to the use of precision agricultural technologies, the use of digital platform and tools, the use of social media and digital marketing was evaluated by AHP method and it was understood that the use of precise agricultural technologies was in the first place.
The Effect of Objective Social Isolation on Depression among College Students during the COVID-19: Focused on Mediation Effect of Resilience

본 연구의 목적은 COVID-19 펜데믹 상황에 있는 대학생을 대상으로 객관적 사회적 고립과 우울 사이에서 회복탄력성의 매개효과를 검증하는데 있다. 본 연구를 위해 K 지역에 소재하는 대학 재학생 282명을 대상으로 객관적 사회적 고립, 우울, 회복탄력성 척도를 사용하여 조사하였으며 설문조사는 2022년 4월부터 3주에 걸쳐 실시되었다. 본 연구에서는 변인 들 간의 관계를 확인하기 위해 구조방정식을 사용하였고, 연구결과는 다음과 같다. 첫째, 객관적 사회적 고립은 우울과의 관계에서 정적 상관관계가 있었다. 둘째, 대학생의 객관적 사회적 고립은 회복탄력성과 정적인 상관을 나타냈다. 셋째, 대학생의 회복탄력성은 우울과 부적인 상관관계가 있었다. 마지막으로, 대학생의 객관적 사회적 고립이 회복탄력성을 통해 우울에 완전 매개 효과가 나타나는 것으로 확인되었다. 즉, 객관적 사회적 고립이 우울에 직접적인 영향을 미치는 것이 아니라 회복탄력성을 매개로 우울에 영향을 미쳤다. 따라서 코로나에 따른 상황적 요인들을 고려하여 우울을 감소시키고, 회복탄력성과 같은 긍정적 대처 요인들을 개발하는 것이 필요할 것이다. 본 연구는 코로나를 경험한 대학생의 정신건강 증진에 기초자료를 제공하고 우울을 예방할 수 있는 프로그램 개발에 기여할 수 있을 것이다. This study investigates the mediating effect of resilience between objective social isolation and depression in college students who had experienced the COVID-19 pandemic. The survey was conducted in April 2022 for three weeks, enrolling 282 college students in universities located in the K province. The students were evaluated in tests that measured objective social isolation, depression, and resilience. Structural equation modeling analysis was applied to determine the relationship among variables. We found that objective social isolation was positively correlated with depression and resilience. The resilience of college students was found to have a negative correlation with depression. Finally, we confirmed that the objective social isolation of college students was fully mediated through resilience. This implies that depression was not affected directly by objective social isolation; rather, the resilience of the subject had a direct correlation with depression. Our results indicate the necessity to develop positive coping factors, such as resilience, that can reduce depression when considering situational factors arising due to COVID-19. We believe that the results of this study can contribute to the development of a program that provides basic data to promote the mental health of college students who have experienced COVID-19 and help prevent depression.