Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN MULTIMEDIA INTERAKTIF BERBASIS ANDROID PADA MATERI PERISTIWA PROKLAMASI KEMERDEKAAN INDONESIA DI SMAN 1 SUNGAI TEBELIAN

This study aimed to develop an android-based interactive multimedia on the events surrounding the proclamation of Indonesian independence at SMAN 1 Sungai Tebelian, besides that the research also measures the effectiveness of the implementation of the interactive multimedia that has been developed. This research used Research and Development (R&amp;D) method. The interactive multimedia consisted of 2 screens, namely the login page and the home page. The login page contains the school logo, as well as a login menu that can be accessed by students using the NISN and Password, after successfully entering the home menu. The contents of the home menu consist of instructions for using the application, developer profile, followed by student presence. There is a KD menu and learning objectives, teaching materials designed using Canva, and, equipped pictures. On the home screen, there are also learning videos, interactive quizzes, a virtual presentation of the national proclamation museum, and reflections on learning. Furthermore, related to effectiveness in the Pretest and Posttest, the average result of the Pretest was 53.67 and the Posttest was 82.83 so the N-Gain test obtained a result of 0.62 and it can be concluded that Android-based interactive multimedia improves student learning outcomes, including into the medium category. The results of the t-test showed a sig value of 0.000 so the value was less than 0.05 so there was an influence in the use of interactive multimedia on students. on the effect size test, the result was 3.70 and it was concluded that interactive multimedia based on android, the material for the proclamation of Indonesian independence had an effect on the strong effect category or very strong.&#x0D; .
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS GOOGLE SITES PADA PEMBELAJARAN SBDP KELAS IV DI SD 1 DASAN GERES

Elementary school learning requires learning media to be able to convey the learning objectives desired by the teacher, including SBDP learning. SBDP learning is often accompanied by monotony and participants are not interested in learning it because it is considered difficult. So you can use Google Sites for the learning process, Google Sites media is media that is easy to use for all groups, and Google Media is a container in the learning process in the 4.0 era for students. The purpose of learning is to see the effectiveness of using google sites media in elementary schools, and whether google sites media can attract students' learning interest. This research uses R&amp;D research or the ADDIE model. The results of the research average percentage of 72% and 75% are in the good category. The media that was validated was then tested on Gueu and students obtained an average percentage of 85%, while in the small group trials, it was 88%, and in the large group it was 81.2%. Including practical media and can attract students' learning interest.&#x0D;
PENDAMPINGAN GURU MELALUI KEGIATAN PENYULUHAN TERHADAP LABORATORIUM VIRTUAL BERBASIS PhET

This community service (PKM) activity is in the form of counseling for MIPA teachers of UPTD SMPN 3 Peusangan through training activities regarding virtual laboratories. Implementations of PKM activities for teachers were carried out through counseling in advance, and then proceed with the training. The benefits obtained from this community service activity include teachers being able to apply learning through a PhET-based virtual laboratory in learning and increasing students' abilities to continue to innovate to think logically, critically and creatively. The use of PhET simulations can help teachers easily explain subject matter to students, teachers can also directly explain abstract subject matter as proven through simulations. The PhET application can also prove things that are difficult to see from practicums carried out in real laboratories, and can even be used online or offline. The objectives of implementing this PKM activity were: 1) increasing teacher awareness of PhET-based virtual laboratory learning media, and 2) motivating students in learning, especially in mathematics and science lessons. The method of implementing the activity used the Focus Group Discussion (FGD) method. From the results of the implementation of outreach activities for PhET-based virtual laboratories for MIPA teachers at UPTD SMP Negeri 3 Peusangan it can be seen that: 1) teacher awareness of PhET-based virtual laboratory learning media has increased; and 2) in learning students are very enthusiastic about expressing their ideas.
PROGRAM MUSHOLLA CERDAS DALAM UPAYA MENINGKATKAN PARTISIPASI WARGA UNTUK BERKEGIATAN DI TEMPAT IBADAH

Musholla or surau is a place that resembles a mosque with a smaller form and often used as a place for religious activities, such as congregational prayers, reciting the Koran, and various religious activities for Muslims. This community service activity was held in Buluh Cina Village, which is a Tourism Village in Kampar Regency, Riau Province. One of the musholla in the village is the Musholla Al-Aqsa. When the service team conducted an initial survey, the condition of the musholla was deserted and there was a shortage of teaching staff and musholla managers, even though the musholla was in the middle of the village and was always passed by residents and travelers. Based on this situation analysis, the service team created assistance activities for residents, namely the smart musholla program which aimed to increase residents' participation in enlivening the musholla. The programs that have been carried out including maghrib recitation, five daily prayers in congregation, dawn prayer studies, Isha studies, evening goro, and musholla management. The service team carried out this activity for one month, and based on observations before and after the activity, there was an increase in the participation rate of residents of various ages in enlivening the Musholla Al-Aqsa.
Features of the expression of value-semantic determination of leadership among students of different fields of training.

This article analyzes the difference in the severity of value-semantic predictors that influence the manifestations of leadership among students. The study involved students in 5 areas of specialties: economists; political scientists; philologists; lawyers; journalists. The relevance of the study is due to a number of factors. The first is the lack of theoretical and methodological elaboration and conceptually categorical justification of the leadership problem, which makes it difficult to conduct scientific research in this area. The second is the need to educate leaders who will be important figures in modern society, in demand not only from business and production, but also from the state, which is ready to support them. The third is information pressure on young people from the outside, which provokes the emergence of new destructive tendencies. The analysis of variance is carried out. The results of testing according to 6 methods are presented by indicators of rank criteria. Students-journalists have marked the expression of value-semantic predictors. It is recommended for journalism students to actively develop and strengthen their leadership potential. It is important to be aware of your values and beliefs, as well as learn to build effective communication strategies. The revealed features of the value-semantic determination of leadership in each specialty provide a platform for creating individual and group programs of correction and development of leadership skills. These results can also be used as methodological tools in the planning and implementation of educational work with students.
The Use of Website-Based PBL Model to Improve the Interest and Motivation in Learning Class X Students

Understanding the material to be delivered and having the right teaching strategy will create a good teaching and learning process by using an interesting learning model so as to increase students' interest in learning in taking lessons and completing assignments given by the teacher (Gulo, 2022). Based on this statement, the use of E-LKPD, flipbook, liveworksheet, wordwall, Canva aims to increase students' interest and motivation in learning. The data collection technique aims to get complete, concrete and real data for the intended class, then I found some unique things that can be further investigated related to interest and motivation to learn. The technique used to obtain the data is to use a test instrument in the form of a post test on Biology material. This data analysis technique uses simple quantitative research with the help of Microsoft Excel. This data processing is done by calculating the average of the overall post test scores. The indicator of success in this study is if the activities of students in each cycle experience an increase and learning outcomes reach the Minimum Criteria of Mastery Learning(KKM), which is ≥ 77 (Arisandi. 2022). in cycle 1 there were only 9 students whose posttest scores showed a completeness score, which exceeded the Minimum Criteria of Mastery Learning(KKM) score. In cycle II there were 14 students whose grades exceeded the Minimum Criteria of Mastery Learning(KKM) score, and in cycle III there were 19 students whose grades exceeded the Minimum Criteria of Mastery Learning(KKM) score.
Managing DOM Events

In the last chapter, you learned about rendering or, more generally, how to draw DOM elements that match your data. But a web application is not a painting; its contents change over time. The cause of these changes is events.
The Psychological Factors Impacts on the Students’ Speaking Skill

English speaking skill can be difficult for students in any educational levels and environments to acquire. The challenge is also encountered by the students of the Islamic-based schools. This is caused by several factors, one of which is psychology. With the aforementioned problem, the researchers intended to find out how the psychological factors could affect students’ speaking ability. The investigation of this study focused on exploring the impact of anxiety, self-confidence, motivation and shyness on Islamic boarding school EFL learners’ speaking abilities. The research design is qualitative with observations, questionnaires, and interviews employed as the research instruments. Based on the analysis, state anxiety appeared to be the main problem for the students. Shy pupils avoid speaking, offer brief responses to queries in the target language, and prefer to remain alone. They have modest communication challenges, are sluggish to express their emotions, and often do not respond when others express their emotions. Self-confidence has an influence on pupils' speaking abilities. In other words, students with high self-confidence are more likely to speak English better than those of with low self-confidence. Another psychological factor - motivation could be influenced by two aspects: internal and external forces. The study's description is likely to be utilized as a consideration for other researchers conducting more research on psychological aspects influencing students' speaking ability in the English classroom.
DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery. To improve the accuracy of the field-road segmentation, this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost. DR-XGBoost takes only a small amount of agricultural machine trajectory features as input. Firstly, the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator. Secondly, the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training. Thirdly, it trains XGBoost to complete the trajectory segmentation. To evaluate the effectiveness of DR-XGBoost, we conducted a series of experiments on a real trajectory dataset of agricultural machines. The model achieves a 98.2% Macro-F1 score on the dataset, which is 10.9% higher than the previous state-of-art. The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem. Keywords: trajectory segmentation, feature extraction, recursive feature elimination, time window, XGBoost DOI: 10.25165/j.ijabe.20231603.8187 Citation: Xiao Y Z, Mo G Z, Xiong X Y, Pan J W, Hu B B, Wu C C, et al. DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination. Int J Agric & Biol Eng, 2023; 2023; 16(3): 169–179.
A Ubiquitous Model of Emotional Tracking in Virtual Classes: From Simple Emotions to Learning Action Tendency

Researchers in the field of ubiquitous learning assert that emotional presence management leads to promising results in the teaching-learning process. However, are there predominant emotions in virtual classes How can emotional clusters be created Is it possible to obtain action tendencies in virtual classes In response to these questions, we propose a ubiquitous emotional model for virtual classes, recording the simple emotions from participants faces, checking whether these emotions can produce emotional clusters, and using these clusters to infer action tendencies within the context of virtual classes. The experimentation was carried out in a real virtual class. To evaluate the model, the experts verified the results of the ubiquitous model, and it is confirmed that the action tendency obtained by the model coincides with the criteria of the experts. Inferring these action tendencies are very important, since the teacher, unlike a face-to-face model, has difficulty observing all the students with the use of cameras in virtual classes, making it difficult to understand the students behavior online.
Mobile-Assisted Language Learning to Support English Language Communication among Higher Education Learners in Thailand

The ability to use English has become essential for functioning in the 21st century; not only the basic skills (reading, writing, listening, and speaking) but broader skills in communication and collaboration will be necessary for future success in global environments. Digital transformation in learning via mobile devices helps create authenticity in English language education. They can interest and engage learners, both in and out of the classrooms, while promoting uninterrupted learning, especially during the COVID-19 pandemic. Moreover, advances in mobile applications and virtual learning environments have become important components of developing English language skills, allowing for asynchronous learning in convenient, flexible, and interactive environments on any of a learner’s devices. This paper reports the results of using a mobile-assisted language learning interactive environment (MALLIE), a chatbot-based application, to support the development of English language communication skills. Quantitative data were acquired from 546 higher education learners in Thailand. The information was used for the exploratory factor analysis which shall serve as a baseline for innovation development. The study briefly examined the students’ experiences with any type of mobile learning before focusing on the students’ and a group of experts’ ratings of the intervention in this study. Preliminary studies were conducted with 10 students and experts to identify factors they believed were most relevant for measuring the effectiveness of the MALLIE for English language learning in a virtual learning environment. Exploratory factor analysis of 70 variables extracted four factors with loadings that exceeded .30: MALL, VLE (virtual learning environment), 4Cs for the four basic components of language learning, and ELCS for English language communication skills; the factor loadings ranged between 0.55 and 0.81. Additionally, in-depth interviews were conducted to collect qualitative data, and the results of preliminary studies were used to design and develop the MALLIE application and test its learning effectiveness. Next, an opinion survey was administered related to a group of 105 students and experts regarding their acceptance of the MALLIE and their intentions to use it or something similar in the future; the main topics of the survey related to the perceived usefulness of the MALLIE, its ease of use, respondents’ attitudes towards its use, peer influences on respondents’ use, and respondents’ use behaviour and intention to use. The aim of the study’s surveys and analyses was to assess the effectiveness and efficiency of learning the English language via mobile language learning applications available for use on any device.
K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Kubernetes is an orchestration platform designed for containerized applications,allows the application provider to scale automatically to match the flfluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures.However, after running for a long time, Kubernetes clusters often suffer from uneven system load, leading to a performance decline. To address this issue, a load balancing strategy called K-TAHP, based on TOPSIS and AHP, is proposed. This strategy takes cpu, memory, and bandwidth usage as load factors and uses K-TAHP to construct load evaluation. By employing a warning module and a migration module, it migrates high-load pods from overloaded nodes to nodes with lower loads, thus improving load balancing in the Kubernetes cluster and resolving performance degradation caused by load imbalance after prolonged cluster operation. Experimental results demonstrate that the K-TAHP load balancing strategy effectively enhances load balancing in Kubernetes clusters, addresses load imbalance issues after long-term operation, and ensures uninterrupted pod services during migration, thereby maintaining cluster performance.
Global and Local Interactive Perception Network for Referring Image Segmentation

The effective modal fusion and perception between the language and the image are necessary for inferring the reference instance in the referring image segmentation (RIS) task. In this article, we propose a novel RIS network, the global and local interactive perception network (GLIPN), to enhance the quality of modal fusion between the language and the image from the local and global perspectives. The core of GLIPN is the global and local interactive perception (GLIP) scheme. Specifically, the GLIP scheme contains the local perception module (LPM) and the global perception module (GPM). The LPM is designed to enhance the local modal fusion by the correspondence between word and image local semantics. The GPM is designed to inject the global structured semantics of images into the modal fusion process, which can better guide the word embedding to perceive the whole image's global structure. Combined with the local-global context semantics fusion, extensive experiments on several benchmark datasets demonstrate the advantage of the proposed GLIPN over most state-of-the-art approaches.
Spectrum Recommendation in Cognitive Internet of Things: A Knowledge-Graph-Based Framework

As an intelligent approach for efficient spectrum utilization in Cognitive Internet of Things (CIoT), spectrum recommendation is interesting but largely unexplored, especially in the few-shot scenario of inference on unknown ratings. For the incompetence of data-driven approaches, we consider playing a guiding role of domain knowledge by introducing it into this few-shot inference problem in the high-frequency (HF) communication, as HF communication is a case of CIoT. Firstly, a knowledge-graph-based framework for spectrum recommendation is developed to organize and represent the knowledge of HF. The knowledge graph (KG) is modeled as an undirected graph for the convenience of extracting and reasoning knowledge. Inference on channel qualities for spectrum recommendation is further converted to the node regression problem on the undirected graph. Then, inference schemes based on graph convolutional network (GCN) are proposed to solve the node regression problem, where we transfer knowledge extracted from the complete data of the source domain to the few-shot inference scenario of the target domain. Experiments on both synthetic data and real data demonstrate the effectiveness of the proposed GCN-based inference algorithm with knowledge transfer.
Domain-aware Prototype Network for Generalized Zero-Shot Learning

Generalized zero-shot learning(GZSL) aims to recognize images from seen and unseen classes with side information, such as manually annotated attribute vectors. Traditional methods focus on mapping images and semantics into a common latent space, thus achieving the visual-semantics alignment. Since the unseen classes are unavailable during training, there is a serious problem of recognition bias, which will tend to recognize unseen classes as seen classes. To solve this problem, we propose a Domain-aware Prototype Network(DPN), which splits the GZSL problem into the seen class recognition and unseen class recognition problem. For the seen classes, we design a domain-aware prototype learning branch with a dual attention feature encoder to capture the essential visual information, which aims to recognize the seen classes and discriminate the novel categories. To further recognize the fine-grained unseen classes, a visual-semantic embedding branch is designed, which aims to align the visual and semantic information for unseen-class recognition. Through the multi-task learning of the prototype learning branch and visual-semantic embedding branch, our model can achieve excellent performance on three popular GZSL datasets.
Inverted Pendulum Control: A Comparative Study from Conventional Control to Reinforcement Learning

The rise of deep reinforcement learning in recent years has led to its usage in solving various challenging problems, such as chess and Go games. However, despite its recent success in solving highly complex problems, a question arises on whether this class of method is best employed to solve control problems in general, such as driverless cars, mobile robot control, or industrial manipulator control. This paper presents a comparative study between various classes of control algorithms and reinforcement learning in controlling an inverted pendulum system to evaluate the performance of reinforcement learning in a control problem. A test was performed to test the performance of root locus-based control, state compensator control, proportional-derivative (PD) control, and a reinforcement learning method, namely the proximal policy optimization (PPO), to control an inverted pendulum on a cart. The performances of the transient responses (such as overshoot, peak time, and settling time) and the steady-state responses (namely steady-state error and the total energy) were compared. It is found that when given a sufficient amount of training, the reinforcement learning algorithm was able to produce a comparable solution to its control algorithm counterparts despite not knowing anything about the system’s properties. Therefore, it is best used to control plants with little to no information regarding the model where testing a particular policy is easy and safe. It is also recommended for a system with a clear objective function.