Reviewerly/exHarmony huggingface dataset:
Fostering Lifelong Learning: Interplay of Self-Concept and Self-Regulation among Adolescents

This correlational quantitative study investigates the relationship between self-concept and self-regulation in learning among middle school students. With a sample of 270 respondents from SMPN 2 Taman Sidoarjo, employing proportional stratified random sampling, the research utilizes the Pearson correlation test for analysis. The study reveals a positive association between students' self-concept and self-regulation in learning (r = 0.357; p &gt; 0.05). The independent variable contributes 12.7% to the dependent variable, implying that 87.3% of variance is attributed to other factors. These findings emphasize the significance of nurturing a positive self-concept alongside promoting self-regulation strategies to enhance students' cognitive development and long-term learning success.&#x0D; Highlights:&#x0D; &#x0D; Positive Self-Concept and Learning Success: This study explores the positive relationship between self-concept and self-regulation in learning among adolescents, highlighting the impact of a positive self-concept on students' overall learning outcomes.&#x0D; Quantitative Correlation Analysis: Utilizing a correlational quantitative approach, the research investigates the interplay between self-concept and self-regulation, offering insights into the strength of their association within the context of student learning.&#x0D; Promoting Lifelong Learning Skills: The findings underscore the need to foster a positive self-concept and provide effective self-regulation strategies to adolescents, as these factors contribute significantly to cognitive development and long-term learning success.&#x0D; &#x0D; Keywords: Self-concept, Self-regulation, Learning outcomes, Adolescents, Correlational research.
Effect of Star Board Rewards on Learning Motivation of 4-5 Year-Old Children: A Quantitative Experimental Study

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study investigates the impact of providing star board rewards on the learning motivation of children aged 4-5 years in Kindergarten Aisyiyah 09 Kranji Lamongan. Using a quantitative experimental approach with a typo one group pretest-posttest research design, the research was conducted in an experimental class comprising 13 students. A motivational questionnaire with 16 questions was used as the assessment instrument, and data collection occurred through pretest and posttest evaluations. The data were analyzed using paired sample t-test, revealing a significant increase in the average value of children's learning motivation by 35.1% from the pretest value to 44.1% in the posttest. Consequently, the findings indicate that giving star board rewards positively influences children's learning motivation (p &lt; 0.05). The implications of this research contribute to our understanding of effective teaching strategies and provide valuable insights for educators and policymakers seeking to enhance children's learning motivation in early education settings.&#x0D; Highlight:&#x0D; &#x0D; The study examines the impact of star board rewards on learning motivation in young children.&#x0D; A quantitative experiment with a typo one group pretest-posttest design was used for data collection.&#x0D; Significant improvement in children's learning motivation was observed after the implementation of star board rewards.&#x0D; &#x0D; Keyword: Star Board Rewards, Learning Motivation, 4-5 Years Old, Kindergarten, Quantitative Experiment&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Introduction to Places of Worship of Religious People in Indonesia Augmented Reality Based As Learning Media for Early childhood

This study aims to introduce an innovative method for religious learning among early childhood in Indonesia by leveraging augmented reality (AR) technology. The research utilizes marker-based tracking, which does not require high-performance Android devices, to create a seamless interaction between virtual religious objects and the real world. The multimedia development lifecycle method is employed in application development, successfully operating on Android devices with versions 7.0 and above. The results demonstrate relatively good marker quality even when printed at A4 and F4 sizes. This research underscores the potential of AR technology as a powerful tool for instilling religious knowledge and habits in young children, offering a promising avenue for future educational interventions.&#x0D; Highlights:&#x0D; &#x0D; AR Enhances Early Religious Education: Augmented reality technology offers an engaging and effective platform for imparting religious knowledge to young children.&#x0D; Marker-Based Simplicity: The use of marker-based tracking simplifies AR implementation, making it accessible even on lower-end Android devices.&#x0D; Potential for Future Interventions: This research highlights the promising potential of AR in early childhood education, paving the way for innovative pedagogical approaches.&#x0D; &#x0D; Keywords: Augmented Reality, Early Childhood Education, Religious Learning, Marker-Based, Tracking, Multimedia Development
Comment Sentiment Analysis of JNE Using K-Nearest Neighbor (KNN) Method on Twitter

Social media has evolved into a prominent public space for virtual criticism, particularly on platforms like Twitter, facilitated by widespread smartphone usage. Netizens utilize Twitter as an effective communication channel due to its accessibility and vast reach. This study focuses on sentiment analysis of comments from the public on Twitter, aiming to expedite the acquisition of accurate information about the general sentiment towards JNE (a logistics company). The K-Nearest Neighbor (KNN) classifier is employed, employing the TF-IDF weighting method to classify Indonesian language comments and assess the achieved accuracy.&#x0D; Highlights:&#x0D; &#x0D; Study focused on sentiment analysis of Twitter comments concerning JNE services using the K-Nearest Neighbor (KNN) method with Indonesian language text.&#x0D; Employed the TF-IDF weighting to classify comments and achieved an impressive 90% accuracy in sentiment analysis.&#x0D; The obtained classification proves valuable in evaluating public perception of JNE's services based on feedback from the social media community on Twitter.&#x0D;
Unity3D FPS TB CARE Game for Tuberculosis Awareness among Digital Native Children

The present study focuses on the development of the TB CARE game, with the aim of addressing the limited knowledge about tuberculosis (TB) in the community, particularly among children. Considering the digital native nature of generation Z, games are adopted as an appropriate medium for dissemination. Employing the observation method, specifically blackbox testing, the study evaluates the game's performance through tests and questionnaires. The findings indicate a significant success rate of approximately 93.5%, signifying the game's efficacy in accordance with the projected outcomes. The TB CARE game exhibits promising potential for future advancements in line with evolving science and technology&#x0D; Highlights:&#x0D; &#x0D; Development of engaging Unity3D FPS TB CARE game to address TB knowledge gap among children.&#x0D; Successful implementation of blackbox testing, with approximately 93.5% game feasibility.&#x0D; Promising potential for future advancements in line with evolving science and technology.&#x0D;
GPT AI Chat: Revolutionizing Education for Civil Engineering Student Performance

This literature review investigates the influence of Chat GPT AI on the effectiveness of the civil engineering curriculum and student performance. The study explores the use of Chat GPT AI in education and emphasizes the significance of rubric assessment in evaluating student achievements. The findings reveal that incorporating Chat GPT AI can significantly enhance the learning process by providing prompt and accurate responses to students' queries. However, the study emphasizes the continued importance of human interaction in the assessment process, as rubric assessment remains crucial for evaluating student performance and fostering motivation for better outcomes. The implications underscore the need to align the curriculum with industry standards and leverage technology to enrich the learning experience. These findings hold potential for educators and policymakers seeking to enhance educational quality and produce highly qualified civil engineering graduates.&#x0D; Highlights:&#x0D; &#x0D; Chat GPT AI: Improving Efficiency - The study explores the impact of Chat GPT AI in civil engineering education, highlighting its potential to enhance the efficiency of the learning process by providing quick and accurate answers to students' questions.&#x0D; Importance of Rubric Assessment - The research emphasizes the crucial role of rubric assessment in measuring student performance and motivating them to achieve better results, highlighting its significance alongside the integration of technology.&#x0D; Adapting Curriculum and Utilizing Technology - The study highlights the importance of adapting the curriculum to meet industry needs and standards, while also leveraging technology, such as Chat GPT AI, to enhance the learning experience and improve the quality of civil engineering education.&#x0D; &#x0D; Keywords: Chat GPT AI, civil engineering education, curriculum efficiency, student performance, rubric assessment.&#x0D;
Sistem Informasi Pengajuan Judul Skripsi Berbasis Online Di Fakultas Ilmu Sosial

The development of science and technology along with the times can support all processes and activities carried out by humans. A computer is a sophisticated tool or electronic object that is broadly composed of hardware and software that work together in processing data entered or provided by the user.&#x0D; Computers have many advantages, they can process data such as the form of numbers, letters, sounds, symbols, and others. With computer technology, data processing will be easier and more efficient so the users do not take a long time to manage data. &#x0D; The Communication sciences Program Studies, Faculty of Social Sciences, University of Dehasen Bengkulu, is still not effective and efficient in terms of campus services, especially for students who want to submit final assignments, especially when submitting thesis titles because they are still using the manual method. By the study program, the files are recaptured one by one into Microsoft Excel, so there is a very high risk of data input errors due to human error, not to mention the possibility of duplicate titles that may occur, where titles that have been submitted can be submitted again by other students. &#x0D; This process needs to be supported by the implementation of technology, for example the use of the web as an implementing medium. The author uses the PHP programming language and MYSQL database which aims to build an information system for submitting a thesis title which is expected to support and facilitate the process of submitting a thesis title and easily obtain information about campus services, especially when submitting a thesis at the University of Dehasen Bengkulu..
Problem Based Learning Model and Cognitive Style on Science Literacy in Fifth Grade Elementary School Students

Education has an important role in improving the quality of life for the younger generation. Quality education can be seen from in education, students should be able to think critically in the context of problem-solving, especially with scientific literacy skills. However, in the learning process in elementary schools, it was found that learning was still teacher-centered and needed more learning models, so the learning patterns used could not develop scientific literacy. This study aimed to analyze the impact of problem-based learning models and cognitive styles on scientific literacy in fifth-grade elementary school students. This type of research is quasi-experimental with a nonequivalent post-test-only control group design. The research population was fifth-grade students. The data collection in the study was carried out using the test method. Data collection instruments were multiple-choice tests for scientific literacy and MFFT tests for students' cognitive styles. The statistical analysis used to test the hypothesis uses two-way Anava and Tukey's tests as a follow-up. The research results from hypotheses I, II, III, and IV show that Ho is rejected and H1 is accepted, thus indicating that the problem-based learning model and cognitive style affect the scientific literacy of class V students. This study implies that the problem-based learning model can be used as a benchmark for improving students' scientific literacy so that in the future, teachers will always improve students' thinking skills and scientific literacy in teaching and dare to make changes in implementing learning.
Defining Logical Systems via Algebraic Constraints on Proofs

Abstract We present a comprehensive programme analysing the decomposition of proof systems for non-classical logics into proof systems for other logics, especially classical logic, using an algebra of constraints. That is, one recovers a proof system for a target logic by enriching a proof system for another, typically simpler, logic with an algebra of constraints that act as correctness conditions on the latter to capture the former; e.g. one may use Boolean algebra to give constraints in a sequent calculus for classical propositional logic to produce a sequent calculus for intuitionistic propositional logic. The idea behind such forms of decomposition is to obtain a tool for uniform and modular treatment of proof theory and to provide a bridge between semantics logics and their proof theory. The paper discusses the theoretical background of the project and provides several illustrations of its work in the field of intuitionistic and modal logics: including, a uniform treatment of modular and cut-free proof systems for a large class of propositional logics; a general criterion for a novel approach to soundness and completeness of a logic with respect to a model-theoretic semantics; and a case study deriving a model-theoretic semantics from a proof-theoretic specification of a logic.
Proposal of a General Model for Creation of Anomaly Detection Systems in IoT Infrastructures

The inclusion of IoT in our platforms is very common nowadays, however, it is necessary to be able to monitor its proper functioning and correction to avoid harmful effects on the systems that are supplied with the data. For the development of systems that allow the detection of anomalies—ADS, it is necessary to use proposals that systematize the construction of such systems, but in this case, focused on IoT and the underlying problematic as metadata redundant, inadequate formats for the treatment and generation of algorithms, need for preprocessing or the need to change the structure of the data. This work proposes an ADS model that characterizes and choreographs a series of processes, sub-processes, stages and activities involved in the generation of this type of systems. To validate the proposal, the creation of an ADS using the model has been instantiated for the Smart University platform of the University of Alicante.
Digital Bodies

Social and cultural categories have long been seen as combined with the bodily »habitus« and its relevance for societal positions. In these approaches, the performance of the body is considered essential for socializing individuals in modern societies. Therefore, bodily performance as »habitus« is a relevant factor regarding social interactions and the social positioning of individuals and groups. Moreover, culture affects the embodied expression of social positions and concepts - the habitus serves as an indicator of social differences or even as a basis of discrimination. Additionally, digital performance has become a relevant form of interacting with others. Since the pandemic, digital performance has started to affect teaching as a new common form of interaction in higher education and has also become a relevant factor in socializing the participants within their academic fields. In this paper, two dynamics of modern societies are reflected on and considered together: culturalization and digitalization. Both developments lead to the question of what has happened to the two fields mentioned above, i.e., social embodiment by cultural positioning and by (further) digitalizing higher education. This paper thus deals with the following questions: What characterizes embodiment in a digital learning environment? What educational consequences does this have for future education in digital learning environments? To find answers to these questions, this article describes how significatory processes can be conceptualized. In the next step, cultural significations are explained in the field of culturalization and with regard to learning as the embodied production of social meaning. Finally, these findings illustrate how and to what extent culture and digitality are shaping the process from meaning to sense-making in modern societies - and thus in current higher education.
Distilling base-and-meta network with contrastive learning for few-shot semantic segmentation

Abstract Current studies in few-shot semantic segmentation mostly utilize meta-learning frameworks to obtain models that can be generalized to new categories. However, these models trained on base classes with sufficient annotated samples are biased towards these base classes, which results in semantic confusion and ambiguity between base classes and new classes. A strategy is to use an additional base learner to recognize the objects of base classes and then refine the prediction results output by the meta learner. In this way, the interaction between these two learners and the way of combining results from the two learners are important. This paper proposes a new model, namely Distilling Base and Meta (DBAM) network by using self-attention mechanism and contrastive learning to enhance the few-shot segmentation performance. First, the self-attention-based ensemble module (SEM) is proposed to produce a more accurate adjustment factor for improving the fusion of two predictions of the two learners. Second, the prototype feature optimization module (PFOM) is proposed to provide an interaction between the two learners, which enhances the ability to distinguish the base classes from the target class by introducing contrastive learning loss. Extensive experiments have demonstrated that our method improves on the PASCAL-5 i under 1-shot and 5-shot settings, respectively.
On Novel System for Detection Video Impairments Using Unsupervised Machine Learning Anomaly Detection Technique

Recently, the necessity of video testing at the point of reception has become a challenge for video distributors. This paper presents a new system framework for managing the quality of video degradation detection. The system is based on objective video quality assessment metrics and unsupervised machine learning techniques that use the dimensionality reduction of time series. It was demonstrated that it is possible to detect anomalies in the video during video streaming in soft real time. In addition, the model discovers degradations based on the visible correlation between adjacent images in the video sequence regardless the quick or slow change of a scene in the sequence. With additional hardware manipulations on the equipment on the user side, the proposed solution can be used in practical implementations where the need for monitoring possible degradations during video streaming exists.
De Bello Homomorphico: Investigation of the extensibility of the OpenFHE library with basic mathematical functions by means of common approaches using the example of the CKKS cryptosystem

Abstract Cloud computing has become increasingly popular due to its scalability, cost-effectiveness, and ability to handle large volumes of data. However, entrusting (sensitive) data to a third party raises concerns about data security and privacy. Homomorphic encryption is one solution that allows users to store and process data in a public cloud without the cloud provider having access to it. Currently, homomorphic encryption libraries only support addition and multiplication; other mathematical functions must be implemented by the user. To this end, we discuss and implement the division, exponential, square root, logarithm, minimum, and maximum function, using the CKKS cryptosystem of the OpenFHE library. To demonstrate that complex applications can be realized with this extended function set, we have used it to homomorphically realize the Box–Cox transform, which is used in many real-world applications, e.g., time-series forecasts. Our results show how the number of iterations required to achieve a given accuracy varies depending on the function. In addition, the execution time for each function is independent of the input and is in the range of ten seconds on a reference machine. With this work, we provide users with insights on how to extend the original restricted function set of the CKKS cryptosystem of the OpenFHE library with basic mathematical functions.
Automatic extraction of non-bifurcated lunar lineaments from Lunar Reconnaissance Orbiter (LRO) monochromatic images based on a Markov chain method

Lineaments play a critical role in the study of lunar geological evolution. However, lineaments still need to be extracted manually which constrains lunar research. To fill this gap, a Markov chain-based methodology is introduced for automatically extracting vector-based non-bifurcated lineaments from images. The extracted lineaments are formed by successive nodes and line segments, where nodes denote pixel positions and line segments represented connecting lines between nodes. A modified U-net with residual shortcut connections and dilated convolutions is proposed based on the elongated shapes of lineaments to evaluate the probability of nodes belonging to lineaments. Four connection shape features are proposed to describe the connection shapes of nodes and a Gaussian Mixture Model is used to evaluate the probability of line segments belonging to the lineaments based on the proposed features. In the final stage, the lineament probabilities of both nodes and line segments to extract lineaments are considered in the Markov chain. Our method has experimented on a dataset containing 220 samples and 10-fold cross-validation was used to evaluate the performance. Both qualitative and quantitative results indicated that our method can effectively extract non-bifurcated lunar lineaments with arbitrary bending. The method sheds useful light on automatic lineament extraction.
The Impact of Tablet Device Applications on Mathematics Teaching and Learning at Secondary School Level in Saudi Arabia

There has recently been a rapid rise in the use of educational applications (apps) in teaching and learning, and they are increasingly being viewed as a necessary part of instruction. However, they are still relatively new in secondary schools in Saudi Arabia. The purpose of this study was to investigate the impact of apps on mathematics teaching and learning in the 10th grade of secondary school. Also, this study will help higher education institutions to know about the supportive educational apps to continue using the beneficial apps and to support the needs of future teachers to be trained and improve the curriculum to keep pace with technology development in education. The interviews’ findings show the need to consider the integration of new technology carefully prior to launching projects involving a change in the educational system to avoid failure. The integration of educational apps helped the students develop their mathematics learning skills, and the teachers viewed it positively in terms of the impact on teaching mathematics concepts at the secondary school level. The study also investigated the obstacles encountered by teachers and students when using various apps and sought solutions. It highlights the implications of app use and makes recommendations.