Reviewerly/exHarmony huggingface dataset:
Image emotion multi-label classification based on multi-graph learning

Images contain rich information and can induce various emotions in the audience. Image emotion classification aims to identify the emotion categories that images can evoke. It is widely used in mental health assessment, human–computer interaction, etc. There are two main problems in existing image emotion classification methods: (1) Most of them only focus on a single emotion label; (2) The global structural relationship among semantic objects in the image is ignored. Therefore, this paper proposes an Image Emotion classification method based on Multi-Graph Multi-Label learning (IE-MGML). In contrast to the existing approaches, the image is transformed into a graph-based representation by extracting the emotional features of semantic objects and calculating the similarity between the features. The local (semantic objects) features and global structure (relationship among semantic objects) features of the image are fused by the relationship between nodes. Furthermore, the graph representation of an image from the perspective of multiple emotional features is pooled and modeled as a graph bag containing multiple graphs (i.e., multi-graph). In multi-graph learning, the graph kernel directly evaluates a graph-label dependency score to avoid the loss of structural information caused by graph-instance degradation. The bag(image)-label dependency score is obtained by aggregating the graph-label dependency score from different perspectives through the aggregation function. The problem of error accumulation in the learning process is handled by proposing a threshold-based ranking loss objective function. Moreover, the non-convex optimization problem is addressed using a subgradient descent algorithm to deal with the required high-dimensional space computation. Experimental results on three general image emotion datasets show that the proposed method outperforms the state-of-the-art methods.
Representing and Manipulating Large Sequences of Argumentation Labellings

This paper proposes a canonical ordering of arguments within abstract argumentation labellings and two new types of efficient representations of these labellings for use in applications involving the computation of argumentation semantics. The space requirements of the representations are analysed, benchmarked on a class of hard enumeration problems taken from the International Competition on Computational Models of Argumentation (ICCMA), and compared for efficiency. We found that they both offer significant reductions of the memory representation requirements of large labellings, sometimes of up to 75%. We argue that the new way of looking at labellings provided by one of the representations, i.e., by considering repetitions of segment assignments within labellings, paves the way for investigations of new applications in argumentation theory.
Analysis of K-means and K-DBSCAN Commonly Used in Data Mining

In today’s era of big data, the amount of potential information behind the data is huge, So how to dig out the potential information in the data more scientifically and efficiently is bound to be a major research hotspot in the world. To solve this problem, we need data mining. therefore, This paper analyzes two algorithms K-means and k-DBSCAN commonly used in data mining, In the second section, we describe the principle and specific implementation steps of the two algorithms, And in the third section, We contrast the two, enumerate the merits and drawbacks of the two algorithms., and examine the differences between the two, and list three cases to analyze the applicability of each in three cases. A lot of complicated data sets may be processed efficiently using clustering. without class markers, which are widely used in the financial industry, biology, astronomy and other fields And K-means and k-DBSCAN are the main algorithms in clustering. By analyzing and comparing the two algorithms, we can know which algorithm is more appropriate in which scenario.
Difficulties and Demands of Field Trips for Early Childhood Teachers due to the COVID-19 Pandemic

Objectives This study examines the difficulties and requirements of early childhood teachers' field trips due to COVID-19, and through this, aims to provide basic data on field trip methods that can effectively cope with crisis situations such as pandemics. Methods 102 homeroom teachers aged 3 to 5 working at an early childhood education institution located in Yongin-si, Gyeonggi-do participated, and the survey was conducted through an open-ended questionnaire. Specific examples and requirements for difficulties in field trips due to the COVID-19 pandemic were described. Results First, the difficulties of early childhood teachers' field trips due to the COVID-19 pandemic are ‘difficulties in supporting teaching and learning linked to play topics’, ‘difficulties due to parents' various requests and non-cooperation’, and ‘difficulty in selecting a site for field trips’. appeared. Second, the requirements of early childhood teachers for effective field trips in the context of the COVID-19 pandemic are ‘institutional support for safe field trips’, ‘parental awareness improvement support for field trips linked to children's play’, It turned out to be ‘educational support for high-quality field trips’. Conclusions In order to effectively implement field trips in a pandemic situation such as COVID-19, it was discussed that a systematic system is needed so that the government, local governments, early childhood education institutions, and families can communicate and share together.
A Study on the Structural relationship between Self-determination, Learning flow, and Innovative behavior of Office worker Graduate students: Focused on Mediating effect of Positive psychological capital

Objectives The purpose of this study is to understand the structural relationship between self-determination, learning flow, and innovative behavior of office worker graduate students , and to find out the mediating effect of positive psychological capital in the structural relationship. Methods The subjects of the study are 197 male and female office workers who are enrolled in D University, Y University's special graduate school, or general graduate school. Self-determination, learning flow, innovative behavior, and positive psychological capital scales were used as research tools. The collected survey results were analyzed using SPSS 29.0 and Smart PLS 4.0 statistical programs. Results First, The self-determination of office worker graduate students, a significant impact on positive psychological capital and learning flow, but it was not significant impact on innovative behavior. Second, It was found that the positive psychological capital of office worker graduate students had a positive (+) effect on learning flow and innovative behavior. Third, Self-determination has an indirect effect on learning flow and innovative behavior through positive psychological capital. Fourth, In the relationship between self-determination and innovative behavior, positive psychological capital was found to have a complete mediation. Conclusions Through the research results, the partial mediation of positive psychological capital was confirmed in the relationship between self-determination, learning flow, and innovative behavior of office worker graduate students. also, positive psychological capital has a complete mediational effect on learning flows. This study is meaningful in that it has prepared basic data for interventions and solutions necessary for office worker graduate students' learning counseling and job competency development.
A Study on the Risk Factors and Management Plan of Knowledge Industry Center Development Project

1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this study is to preemptively explore the risk factors derived from the development process of the Knowledge Industry Center, which has recently been in the limelight as a new development project product, and to derive risk management measures for them. (2) RESEARCH METHOD As a research method, multi-level decision-making techniques (AHP and Fuzzy-AHP analysis methods) were used to analyze the importance of risk factors in each development stage and to derive risk management plans. (3) RESEARCH FINDINGS Risk management measures in order of importance include securing a construction company with recognition and brand, monitoring policy trends and adjusting business timing by development stage, establishing and verifying plans in preparation for product demand, blocking disputes through guarantee insurance, etc., and reducing risks through interest sharing. 2. RESULTS As a result of the analysis, it was confirmed that the overall importance of AHP and Fuzzy-AHP adjusted overall importance were similar in rank by risk factor. The top 5 risk factors as a result of importance are failure to secure a construction company with high recognition and brand, marketing planning, failure to select a project start time due to failure to predict the real estate economic downturn, failure to set an appropriate development scale that does not consider the future demand of the Knowledge Industry Center, failure to secure business partners (equity investors, financial companies, construction companies), and insufficient funding plan to secure project costs (bridge loans, main PF) appeared in order.
ANDROID PLATFORM OVERVIEW: M-LEARNING APPLICATION FOR COLLEGE EDUCATION

On average, students of the Yogyakarta Nahdlatul Ulama University (UNU Yogyakarta) have difficulty accessing course materials and self-assessments from e-learning platforms without the internet. The author uses a qualitative method by using observation to get direct information about access to course material from the e-learning platform. The mobile learning (m-learning) application prototype was developed through the android platform using the Java programming language. As a result, the use of m-learning can be one of the best modules of mixed learning techniques. The author recommends that UNU Yogyakarta integrate it with their e-learning platform. This m-learning application intends to complement the e-learning system and make students whenever and wherever they want to gain knowledge without the internet via an android phone. Future researchers can connect the m-learning application and other aspects of the e-learning platform.
CNNCalc – An Implementation of a Handwritten Mathematical Equation Solver

Creating a reliable handwritten equation solver using Convolutional Neural Networks (CNN) is a challenging task in image processing and classification. The recognition of handwritten mathematical expressions is one of the most complex challenges in computer vision research because certain characters are segmented and classified, such as polynomial, quadratic, and other mathematical equations. The aim of this study is to develop an application that can solve handwritten equations (arithmetic, quadratic, and trigonometric) and logical operations (logical AND, OR, NOT, NAND, XOR, NOR) using CNN with image processing techniques to achieve high accuracy. Additionally, the application can extract text from images, and users can learn and share educational content on the platform to enhance their knowledge. One of the main advantages of online learning is that it allows users to access updated content and media at any time. The platform also enables users to take tests on various topics and receive instant feedback Users can code in various languages, including C, Python, and Java. Furthermore, the application provides a way for users to monitor their performance and improve their skills continually. The approach used in this project involves horizontal compact projection analysis and a survey for segmentation and binarization. Connected component analysis and integrated connected component analysis methodologies are utilized for character classification using CNN. Character string operation is used for detecting characters in the polynomial. The experimental results demonstrate the effectiveness of this approach in solving a wide range of mathematical equations. This application aims to make learning an enjoyable experience for students, especially in today’s digital age.
An Artificial Hummingbird Optimization Algorithm Incorporating Adaptive Search and Levy Flight

To address the problems of low accuracy of artificial hummingbird algorithm (AHA) and its tendency to fall into local optimum, we propose a new artificial hummingbird optimization algorithm (ALAHA) integrating adaptive search and Levy flight. First, we use kent mapping in the initial stage of the algorithm to make the distribution of hummingbird individuals more uniform; then, we introduce Levy flight as an adaptive weight factor to adjust the search step size in the AHA guided foraging and territorial foraging stages to improve the global search ability of the population; finally, we perform adaptive distance search around hummingbird individuals according to the population convergence to improve the search accuracy of the algorithm. In this paper, 10 classical benchmark test functions are selected to experiment the algorithm and compared with other algorithms to test the performance of the algorithm from different perspectives. The results show that the improvement of ALAHA algorithm has improved in the aspects of merit-seeking ability, stability and robustness. The algorithm before and after the improvement is also experimented with the rolling bearing design, and the results are found to be improved compared with the previous algorithm.
Text Retrieval Method Integrating Scientific Knowledge and Pre-trained Model

The amount of scientific literature is growing year after year in tandem with the Internet’s fast expansion, and effective scientific literature retrieval is helpful for users’ scientific research work. There are unregistered technical terms in scientific literature, which causes the problem of semantic bias in the text representation of traditional retrieval methods, resulting in low retrieval precision, recall, and accuracy. Aiming at the above problems, a text retrieval method that integrates scientific knowledge and pre-trained models is proposed. This method initially mines new words from scientific publications using a novel word mining algorithm based on mutual information and adjacency entropy. And takes them as the full word mask object of the RoBERTa-wwm pre-training model for secondary pre-training, so that the pre-training model can integrate scientific knowledge and improve the ability to understand scientific knowledge. The matching semantic vector is then obtained by first encoding the original text using the secondary pre-training model, and then one-dimensional convolution is used for feature extraction to extract key text features through the Max-pooling layer. The completely linked layer is then mapped to the text characteristics, and the matching scores between texts are computed. According to experimental findings, our technique outperforms numerous other models on text retrieval tasks in the scientific sector.
A Case Study on Liberal Arts Education in Smart Media Literacy: Discussion on the Ccase of &lt;Understanding Smart Media Contents&gt;

The purpose of this article is to report to the academic community on the case study of &lt;Understanding Smart Media Contents&gt;, a general elective course at Gwangju National University, and to present a model that can be applied in practice or widely referenced for convergent digital media literacy education in the university setting. As a subject that requires contemporary discussions on the cases of today's media platforms and contents, it will provide useful teaching models and data in the field of convergent media contents. In addition, it will help to improve the media literacy competence of learners to self-directly respond to and reflect on the moral and ethical problems caused by various smart media platforms and contents.
Research on personalized recommendation algorithms for the web with knowledge representation

With the rapid development of information technology and the increasing scale of the Internet, a huge amount of data and information has been generated, and people face a huge challenge to get the information they need from it. In order to solve these challenges, personalized recommendation technology has emerged, which can actively recommend items of potential interest to users. The most mainstream personalized recommendation technology is collaborative filtering, which has been applied in various fields and achieved good results. However, its recommendation performance tends to drop sharply when facing data sparsity and cold-start problems. Currently, knowledge representation techniques have attracted wide attention from academia and industry, and have been applied to recommender systems and other fields, and have made important breakthroughs. To solve the problem of data sparsity and improve recommendation accuracy, this paper introduces knowledge representation into neural collaborative filtering model and proposes a neural collaborative filtering model assisted by knowledge graph embedding. By alternating the training of the knowledge representation module of the recommendation module, the knowledge representation module assists the training of the recommendation module, which effectively improves the rating prediction effect. Through experiments, it is shown that the model not only improves 9.46% and 10.18% in MAE and RMSE respectively over the UserCF method, but also effectively alleviates the data sparsity problem.
Efektivitas Permainan Pancing Huruf Dan Gambar Terhadap Keterampilan Membaca Pada Anak Di Taman Kanak-Kanak Hang Tuah Padang

This research was motivated by the problems encountered in the Hang Tuah Padang Kindergarten, Padang, which showed low reading skills in children due to the inappropriateness of the media in learning to read used by the teacher. This study aims to find out how effective the fishing game with letters and pictures is on children's reading skills at Hang Tuah Padang Kindergarten. The type of research used is a quantitative method with the type of experiment or quashi experimental (pseudo-experiment). The data collection technique in this study is in the form of a test. The population in this study were the children of Hang Tuah Padang Kindergarten. The sampling technique used was purposive sampling technique. Experimental class in class B1 with fishing games for letters and pictures and B2 with letter cards. Data analysis techniques in this study used the Normality Test, Homogenity Test, Hypothesis Test, and Effect Size Test with the help of the SPSS Windows 22 application. The results of the study were obtained based on data analysis that the sig (2-tailed) value was 0.002 &lt;0.05 and the results of the effect test size 1.67 is included in the strong category and shows that there is a significant difference between the reading skills of the experimental class children using letter and picture hooks compared to the control class using letter cards.
THE PERFORMANCE ON USING CONJUNCTION BY THE FIFTH SEMESTER STUDENTS OF ENGLISH DEPARTMENT AT MUHAMMADIYAH UNIVERSITY OF MAKASSAR

This study aimed at finding out: 1. How are the students’ performance on the use of coordinating conjunction, 2. how are the students performance on the use subordinating conjunction. The method in this research was descriptive quantitative, the reseacher chooses subject class C by the fifth-semenster studnets of English Department from Muhammadiyah University of Makassar, consisting of twenty students. To got the data of this research were collected through tests and interviews by google form.The test results from students, that students know better the use of coordinating conjunction, many students answer correctly with an average highest score of 670, but the students difficult to distinguish the use of conjunction which has the same meaning, like but and yet, also for and because. for subordinating conjunction. shows, the students low understand the use of that conjunctions, many students answer incorrectly, expecially conjunction althogh, after and where. an average highest score of 605, the students still confused the function of that conjunction itself.
What is a capable guardian to older fraud victims? Comparison of younger and older victims’ characteristics of online fraud utilizing routine activity theory

The paper compares victim group characteristics: we test routine activities theory to compare the differences in online fraud vulnerabilities of victims aged 18-54 and victims of 55 and above.A representative sample of US citizens 18 and above was collected in October 2020. Victims under 55 encompassed 35.3% (n = 915), victims 55 and above 12.9% (n = 334) of the total sample (n = 2,589). We utilized non-parametric statistical methods for testing whether older and younger victims' characteristics can be derived from the same independent variables.Computer time, computer familiarity, and technical guardians determine online victimization in older individuals, similarly to younger age groups. However, older victims differ in characteristics from younger victims. Seniors were less likely to apply technical guardians such as camera cover, identity theft monitoring, and credit card freeze, even after experiencing online scams. Being a single parent was a protective factor for older individuals, but having a full-time job made older individuals more prone to experience online fraud victimization compared to being retired. In addition, older victims were less likely to report scams than younger ones.Although this research found significant differences between older and younger fraud victims' characteristics, target suitability and capable guardianship must be further investigated and conceptualized when applying routine activities theory for online fraud against older people.
The Use of 3D Virtual Tours Technology in Physics Classes for Teaching Physical Quantities

Virtual Reality (VR) technology offers great opportunities for accelerated development in many educational fields, beckoning with new impulses and breakthroughs, promising the rise of teaching efficiency as well. The challenge for educators is learning how to unleash the teaching potentiality of VR in a useful, safe and controlled way that is adequate for solving current pedagogical issues. By now, there is a solid confirmation that VR is a helpful and necessary instrumentation, at least in physics classes. The paper deals with a look at teaching physical quantities based on VR tour technologies, not in terms of whether or not we should use VR, but in terms of where VR learning physical quantities can and cannot happen. In this paper, we overviewed the idea for teaching physical quantities through 3D Virtual Tours. Here is suggested a systematically constructed example of the VR application delivered in a way that can be parsed and used by teachers and students. The proposed solutions may be of interest to educators, teachers, instructional designers, educational game creators, and enthusiastic students in designing and using virtual tour technologies.
An Access Control Scheme Based on Decentralized Identifiers and Verifiable Credentials in IoT

With the rapid development of information technology, the Internet of Things (IoT) has become more complex, and more and more devices and users are connected to the IoT system. While improving data interactivity and expanding the scope of data sharing, secure access to IoT resources is also facing great risks. Therefore, it is necessary to design an appropriate access control scheme. Firstly, this paper introduces Decentralized Identifiers (DID) and Verifiable Credentials. It is used for authentication and rights management of users and devices, respectively, which makes identity and authority unforgeable. Secondly, this paper uses blockchain technology to support DID and Verifiable Credentials, which can ensure the decentralization, invariance and traceability of data. Finally, using smart contracts enables automatic processing of requester authorization and access requests. The test results show that the scheme has good performance.