Reviewerly/exHarmony huggingface dataset:
Virtual events promoted by the women’s health care league during the covid-19 pandemic

Abstract Objective to analyze the participants' perception of the online events promoted during the first year of the Covid-19 pandemic by the Women's Health Care League (LCSM). Methods cross-sectional, descriptive study, with a quantitative and qualitative approach, developed online with 90 participants in ten events on the theme of women's health, promoted by LCSM in 2020. Data were collected using a semi-structured online form from July to November 2021. For data analysis, IBM SPSS was used, through descriptive statistics of qualitative variables and for the analysis of qualitative data, thematic analysis was used. Results 98,89% of the participants answered that the LCSM events in online format had a positive influence on their academic, professional or personal life. The analysis of the responses allowed the identification of three categories: feeling of “closeness” to the University, description of positive influences, and knowledge beyond the classroom. Conclusion and implications for practice The data from this study allow us to infer the important and necessary role of information technology and academic leagues in the construction of knowledge and in the deepening of reflections and debate on women's health care.
Knowledge Oriented Strategies: Dedicated Rule Engine

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely-used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.
Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.
A Comparison Study on Particle Swarm Optimization (PSO) Algorithms for Data Feature Selection

High-dimensional data presents a challenge for big data analysis and machine learning due to its complexity and size. To alleviate the pain of handling such data, feature selection is a good choice to choose the most relevant and useful features. To do so, there are several strategies to choose from and this work focuses on the Particle Swarm Optimization (PSO) for classifying to famous datasets: the Arrhythmia and the DBWorld e-mails datasets. For the sake of comparison, five PSO algorithms containing different weights are paid attention to and three well-known classifiers namely: decision tree (DT), random forest (RF), and support vector machine (SVM) are performed. The results show that all five PSOs perform well using only approximately 50% of the dataset. Evidently, a “weight” parameter plays a crucial role in the PSO algorithm and clearly deserves further investigation. With a good combination between the weight and the classifier, our experiments indicate that satisfactory results can be obtained.
Penggunaan Media Pembelajaran Audio Visual Dalam Meningkatkan Semangat Belajar Siswa di Madrasah Aliyah Unggulan Arifah Kabupaten Gowa

This research aims to see how the use of audio-visual learning media increases students' enthusiasm for learning at the Unggulan Arifah Madrasah Aliyah, Gowa Regency, with the research objects being students and teachers. The method used in this research is a qualitative research method, which means that the data collected is in the form of words, images and not numbers. In this research, the data sources used by researchers are primary data sources and secondary data sources. The research instruments used were observation guidelines, interview guidelines and documentation. Data collection techniques include observation, interviews and documentation. The data analysis techniques used in this research are data reduction, data presentation and drawing conclusions and verification. From the research that has been conducted, it can be seen that by using audio-visual learning media, it can be seen that students are very enthusiastic and enthusiastic when learning using audio-visual learning media. This is proven by the students' responses being very active, full of enthusiasm and responding quickly to the teacher in answering questions and daring to put forward answers. They also want video learning media to be presented continuously in learning but also supported by brief explanations by the teacher so that teacher-oriented learning will be slightly reduced.
Penerapan Model Pembelajaran Picture and Picture untuk Meningkatkan Kemampuan Menulis Teks Prosedur Siswa Kelas IX SMP Negeri 1 Alasa Talumuzoi

This study aims to improve the ability to write procedural texts of students of class IX B SMP Negeri 1 Alasa Talumuzoi by using Picture and Picture Learning Model. The type of research is Classroom Action Research with the stages of planning, action, observation, and reflection. The research data is in the form of qualitative and quantitative data. The research sample was students of class IX B SMP Negeri 1 Alasa Talumuzoi. The results showed that in cycle I, the average score of students was 68.93 with the qualifications of excellent 7%, good 27%, sufficient 30%, and less 37%. In cycle II, the average score of students was 83.67 with excellent qualifications 33.33%, good 50%, sufficient 16.67%. Based on the results of the study, the application of Picture and Picture Learning Model can improve the ability to write procedure text of students of class IX B SMP Negeri 1 Alasa Talumuzoi.
Logico-Semantic Relations of Korean Quotation Clauses based on Systemic Functional Linguistics

Objectives The purpose of this paper is to identify the types of logico-semantic relations that Korean quotation clauses have with their main clauses.&#x0D; Methods The clause complexing system of systemic functional linguistics separates the logico-semantic relations of quotation clauses and the Taxis into a optional system. This method can solves the previous problems of mixing the two criteria within the framework of embedded clauses.&#x0D; Results First, we argued that the logico-semantic relationship(projection) and clause complexing of quotation clause are independent systems, so that quotation clause can be used as a paratactic, hypotactic, or adverbial clause. we, also, explained that projecting nouns and fact nouns require adjective clauses as complement clause, and some factual cognitive verbs require nominal clauses as complement clause. Second, we explained that Korean quotation clauses can be logically and semantically categorized into locution, idea, and fact projections, respectively. Each of these is governed by the locution verb, the idea verb, and the fact noun in the main clause. In addition, a projecting clause always functions as a proposition or a proposal: thus, projenting clause can be defined as an obligatory bound clause that functions as speech acts of proposition or proposal. Third, from the perspective of systemic functional linguistics, we proposed the morpho-syntactic structures of quotation clauses as paratactic, hypotactic, and adverbial clauses, reflecting the aforementioned logico-semantic types and clause complexing system of quotation clauses.&#x0D; Conclusions The clause complexing system of SFL adopted in this paper is expected to provide a more comprehensive explanation than existing grammar theories because it reflects both logical-semantic types and the Taxis.
A study on how to standardize design forms for the development and implementation of major competency-based curriculum in universities

Objectives This study was conducted to develop a standardized design forms for the development and implementation of competency-based curriculum design model for majors that can be applied to units above the minor level in universities.&#x0D; Methods For this purpose, we explored the meaning and components of competency-based education based on literature review, and developed a curriculum design guide and template that can be applied to curriculum design by considering the characteristics of competency-based curriculum models and backward design models, which are expected to provide appropriate guidance and practical help in the reality of university education. In the course of the study, expert validity assessment was conducted twice.&#x0D; Results As a result, the major competency-based curriculum design model was divided into three stages, reflecting the characteristics of the outputs of each stage: stage 1, defining major competencies; stage 2, defining competency diagnostic tools and evaluation methods; and stage 3, defining a competency-based curriculum roadmap. A total of 12 checkpoints were identified (four for each stage) and a template reflecting the checkpoints for each design stage was proposed.&#x0D; Conclusions The curriculum design checklist and template derived from this study are expected to be of practical help to professors and those responsible for curriculum development and operation in universities to design and operate competency-based curricula and courses.
Educational Significance of Garden Activities Experienced for 3-year-old Infants

Objectives The purpose of this study is to examine the educational meaning and educational growth of proper personality education and environmental education at the educational level through the experience of garden activities.&#x0D; Methods A total of 6 children in Class B, 3 years old at daycare center A located in Incheon, were selected as study participants. Data collection was conducted three times a week from March 17, 2022 to February 10, 2023, with 11-month observation logs, early childhood interviews, picture data, and participatory observation. In accordance with the data analysis procedure, the process of transcription, coding, and subject generation was analyzed by the research method of inductive analysis procedure and interpretive analysis procedure Hatch.&#x0D; Results The educational meaning of gardening activities experienced by 3-year-old children was as follows: ‘The beginning of garden play is exciting,’ ‘The artists of garden play are nature and children,’ and ‘Walking play is for you, me, and us together.’ The educational growth shown by the three children was ‘Growing, growing!’ It appeared as ‘growing as a child with character’ and ‘we grow as guardians of the environment’.&#x0D; Conclusions Through the results of this study, it was confirmed that gardening activities influence the educational growth of sound character and environmental education in 3-year-old children... First, natural activities help children's character education, and second, the experience of natural circulation. Third, sustainable development education begins with small efforts by teachers, so it is necessary to reflect gardening activities in childcare activities for environmental education. Fourth, cooperation between daycare centers and related organizations is needed. There is a need to utilize it. This study is meaningful as an example of how the researcher utilized the garden in the daycare center by reflecting the garden activity in the curriculum to overcome the problems of indoor-centered children's activities in the era of COVID-19.
A Study on the Development of Preliminary Questions for Student Agency Testing of Elementary School Students in 3rd to 6th grades

The purpose of this study is to develop a preliminary question that measures the student agency of elementary school students in the 3rd and 6th grades. To achieve the research purpose, an extensive review of relevant literature was conducted, resulting in the identification of six key components of student agency. These components are 'mindset', 'self-perception', 'intentionality', 'choice-making', 'persistence', and 'interactiveness', which have been further categorized into 37 questions across the six identified areas. Second, based on these draft questions, a Delphi survey was conducted targeting 10 researchers related to elementary education and student agency. Through the first Delphi, the names and questions of each area were revised, and based on this, the second Delphi was conducted. As a result of the second Delphi, 32 preliminary questions in 6 factors were derived: 'growth-mindset', 'self-perception', 'intentionality', 'choice-making', 'persistence', and 'interactiveness'. Third, a preliminary test was conducted on 173 3rd to 6th grade elementary school students to confirm the reliability of the preliminary questions, and it was determined that the questions agreed upon through Delphi were stated in terms that elementary school students could sufficiently understand. Through this process, the preliminary questions were revised into 6 components and 31 sub-questions. Next, to verify the validity of the preliminary questions, a test was conducted on 407 3rd to 6th grade elementary school students based on the revised questions, and then an exploratory factor analysis was performed. As a result of the analysis, the preliminary questions for the student agency test for students in grades 3 to 6 of elementary school are 'growth-mindset (4 questions), self-perception (5 questions), intentionality (4 questions), choice-making (3 questions), and persistence (5 questions), interactiveness (7 questions) and 28 sub-questions were finally derived. Finally, the conclusion revealed the implications and limitations of this study, and suggested ways to utilize the preliminary questions developed in this study as well as for future research.
PEDAGOGICAL CONDITIONS OF FORMING FUTURE PRIMARY SCHOOL TEACHERS' READINESS TO USE DISTANCE LEARNING TOOLS

In the context of the development of scientific and technological progress in the education system with the involvement of modern computer technologies, the field of training future teachers also requires fundamental changes. Among the key competences defined in the new State Standard of Education of Ukraine (2016) is information and communication (information and digital) competence, which includes the confident use of information and communication technologies (ICT) to create, search, process and exchange information in the workplace, in public space and in private communication. ICT competence also includes information and media literacy, programming basics, algorithmic thinking, database manipulation, development of Internet and cybersecurity skills, and an understanding of ethics (e.g. copyright, intellectual property) when working with information [1]. Thus, the new Ukrainian school needs teachers who can ensure the development of information and communication (ICT) skills in prepared and motivated students. One of the most effective ways to achieve this is through the use of distance education technologies. Mastery of distance learning technologies is becoming a prerequisite for further professional development, particularly in the field of education. Currently, research by domestic and foreign scientists is aimed at identifying a number of conditions for the introduction of distance technologies in the educational process in higher education and ensuring its effectiveness [2]. However, it should be noted that at this stage of education development, there is no single list of conditions for the formation of future primary school teachers' professional readiness to use distance education tools.
Achieving Fairness in DareFightingICE Agents Evaluation Through a Delay Mechanism

This paper proposes a delay mechanism to mitigate the impact of latency differences in the gRPC framework—a high-performance, open-source universal remote procedure call (RPC) framework—between different programming languages on the performance of agents in DareFightingICE, a fighting-game research platform. The study finds that gRPC latency differences between Java and Python can significantly impact real-time decision-making. Without a delay mechanism, Java-based agents outperform Python-based ones due to lower gRPC latency on the Java platform. However, with the proposed delay mechanism, both Java-based and Python-based agents exhibit similar performance, leading to a fair comparison between agents developed using different programming languages. Thus, this work underscores the crucial importance of considering gRPC latency when developing and evaluating agents in DareFighting-ICE, and the insights gained could potentially extend to other gRPC-based applications.
Underwater Image Enhancement Based on Shallow Underwater Neural Network

In recent years, due to the importance of underwater image enhancement in underwater robot, underwater vehicle and ocean engineering, more and more extensive research has been done. It has evolved from implementing physics-based solutions to using very cutting edge cnn and GANs. However, these cutting-edge algorithms often come at the cost of high computing power and time, which reduces the efficiency and portability of underwater working equipment using these algorithms. At the same time, these models have harsh requirements on data sets, leading to high cost of training and unfriendly to many underwater operations. Therefore, this paper aims to propose a lightweight neural network structure, Shallow underwater neural network. These neural networks associate the original image directly with the output of each convolutional layer, preserving the original features while enhancing the image and avoiding gradient descent. The experimental results show that the model has a good effect on image enhancement, and the structure is lightweight.
Fighting Game Adaptive Background Music for Improved Gameplay

This paper presents our work to enhance the background music (BGM) in DareFightingICE by adding adaptive features. The adaptive BGM consists of three different categories of instruments playing the BGM of the winner sound design from the 2022 DareFightingICE Competition. The BGM adapts by changing the volume of each category of instruments. Each category is connected to a different element of the game. We then run experiments to evaluate the adaptive BGM by using a deep reinforcement learning AI agent that only uses audio as input (Blind DL AI). The results show that the performance of the Blind DL AI improves while playing with the adaptive BGM as compared to playing without the adaptive BGM.
Person Re-Identification Based on Instance Segmentation and Pose Estimation

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively.