Reviewerly/exHarmony huggingface dataset:
PENGARUH MINAT TERHADAP AKTIVITAS BELAJAR SISWA KELAS VIII PADA MATA PELAJARAN MATEMATIKA DI SMP KRISTEN ENDE

This study aims to determine (1) students' interest in mathematics in class VIII SMP Kristen Ende. (2) Students' learning activities towards mathematics in class VIII of Ende Christian Junior High School. (3) The effect of interest on mathematics learning activities in class VIII of Ende Christian Middle School. This research is associative quantitative research. The subjects of this study were class VIII students with a total sample of 30 people. Factors studied in the form of student interest in student learning activities in mathematics. Data collection methods used in this study were questionnaires and documentation. Data analysis used simple linear regression analysis. The results showed that (1) students' interest in mathematics is 0,843 (84,3 %). (2) The student's learning activity towards learning mathematics is 0,782 (78,2 %). (3) There is a significant effect of the interest variable on the learning activities of class VIII students in mathematics at the Ende Christian Junior High School 23,9 %, while the rest is influenced by other variables. With the regression calculation obtained Y = 32.241 + 0.399 X. The magnitude of the influence of variable X on the Y variable is 0,239 (23,9 %). The significance value of a hypothesis is the truth value of the accepted or rejected hypothesis. Because the value of tcount&gt; t table is .4,221&gt;0,048with a significant value 0,003&lt;0,05, it can be concluded that there is an influence of interest on the learning activities of class VIII students in mathematics at Christian Junior High School Ende.
Young Adult and Canonical Literature Instruction in the High School Classroom: Assessing Students’ Reading Interest

In the high school English classroom, classic novels are taught as cornerstones of the curriculum. Although these canonical works such as &lt;em&gt;To Kill a Mockingbird &lt;/em&gt;(1960) and &lt;em&gt;The Adventures of Tom Sawyer &lt;/em&gt;(1876) are revered for their literary merit, students often find them boring and skim through the readings or decline to read altogether. Young adult literature (YAL), a genre written for teens, may be an effective genre to teach in high school to boost students’ reading interest. This study aims to determine how teaching young adult literature in the high school classroom, as opposed to canonical works, might affect students’ interest in the texts. A survey was administered to 57 high school students ages 15-17, studying YAL in the Southern Tier region of upstate New York. The survey asked students to describe their interest in an assigned YAL book and compare it to their interest in canonical novels that they have read in the past. Results indicate that teaching a combination of YAL and canonical literature may increase students’ reading interest while also broadening their understanding and worldviews. Potential implications of this research include revising the English literature curriculum to accommodate students’ reading interest and diversifying assigned reading lists to incorporate wider cultural perspectives.
HUBUNGAN MINAT BELAJAR DAN PRESTASI BELAJAR MATEMATIKA SISWA KELAS XI SEMESTER II SMAK ST. PETRUS ENDE

This study aims to determine the relationship between interest in learning and learning achievement in mathematics in Class XI Semester II SMAK St. Petrus Ende. The approach used is a quantitative approach. Data analysis used the product moment formula with a significance level of 5%. The results of the study showed (1) the learning interest of Class XI Semester II students of SMAK St. Petrus Ende is known to be in the 33,33% trend category of high, (2) Mathematics learning achievement of Class XI Semester II students of SMAK St. Petrus Ende is known to be in the category of high tendencies of 33%, (3) From the results of the analysis, the sig value is obtained. (2-tailed) 0.000 &lt;0.05 with a Pearson Correlation score of 0.699 so it can be concluded that there is an influence between learning interest and learning achievement in mathematics in class XI students in the second semester of SMAK St. Petrus Ende. Furthermore, the coefficient of determination was tested to see the magnitude of the influence of interest in learning on learning achievement in mathematics and obtained a coefficient of determination of 0.489/48,9%. So it can be concluded that Ho is rejected and Ha is accepted, meaning that there is a relationship between interest in learning and learning achievement in mathematics for Class XI Semester II SMAK St. Petrus Ende. Thus, it is suggested, (1) For schools, it can be used as material for teacher consideration in carrying out the learning process at school (2) For mathematics teachers, it can find out students' interest in learning.
Pengenalan Aplikasi Phet Simulation Sebagai Media Pembelajaran Untuk Materi Konsep Getaran di SMA Teruna Bakti Waena Jayapura

The task of a lecturer is to carry out the Tri Dharma of Higher Education, one of which is to do community service. Service activities have been carried out on the fourth week of April 2022 which is located at SMA taruna Bakti Waena Jayapura, the number of students who attended were 28 students and were accompanied by one of the teachers who taught Physics. The material presented was about the introduction of PhET Simulation Application as learning media for Masses and Springs. The goal to be achieved from this activity is so that students get enlightenment about the concept of Masses and Springs through the PhET Simulation application. The method used is lecture, question and answer, demonstration. The results obtained before using the PhET Simulation application, students had little difficulty understanding the concept of Masses and Springs, but after using the application, students understood the concept of Masses and Springs.
Reinforcement Learning in Robotics: Challenges and Applications

Reinforcement Learning (RL) is at the vanguard of robotics revolution, allowing machines to learn and make choices in complicated environments. This paper explores the symbiotic dating between RL and robotics, focusing at the challenges and programs that shape this interdisciplinary area. The dialogue makes a speciality of the fundamental standards of RL and its integration into robotics, elucidating the specific demanding situations encountered on this merger. The research covers the space among simulation-primarily based schooling and real-world applicability, navigating hardware obstacles, and addressing safety concerns, organising a comprehensive view of the challenges encountered when deploying RL for robotic systems. This paper also offers insights into the various programs of RL in robotics, inclusive of self sustaining navigation, item manipulation, healthcare, and business automation. It investigates how RL algorithms assist robots navigate complex environments, gain item manipulation dexterity, and contribute to healthcare improvements and industrial optimization. Case studies exhibit the software of RL in robotics by means of highlighting a hit implementations and demonstrating the transformative capability of this aggregate. Finally, this paper outlines future instructions, paving the way for persisted innovation and emphasizing the importance of bridging the theoretical advancements and real-international deployment in RL-driven robotics.
Cloud Cost Management and Optimization

As organizations increasingly migrate to cloud environments, the need to manage costs becomes paramount effectively and efficiently. This paper explores the basic principles and techniques of Cloud Cost. Management and Optimization, focusing on Amazon Web Services (AWS) as a key case study The study delves into the understanding of cloud pricing models, construction emphasize matching pricing plans to specific projects for optimal costs Tools such as Explorer facilitate, search and its role in identifying usage and inefficiencies, explore cost controls with budget constraints and governance systems include implementation to reduce unexpected cost overruns and enhance financial predictability with automation and DevOps They are discussed as important factors in streamlining processes, resources efficiency, and improving overall costs. Through a comprehensive analysis of these principles, the study aims to provide organizations with insights they can use to manage the complexity of cloud cost structures, driving sustainable economic growth and maximize the benefits of cloud computing.
Cloud Computing Trends for the Future

As the technological landscape continues to conform unexpectedly, the paradigm of cloud computing stands at the forefront of innovation, shaping the destiny of statistics technology. This evaluation paper gives an insightful analysis of the modern-day traits in cloud computing and anticipates the trajectories in an effort to define its future. Through a complete synthesis of the latest literature, this paper explores key trends, demanding situations, and opportunities in cloud computing. It delves into rising technology consisting of part computing, serverless computing, and quantum computing, dropping light on their transformative potential. Additionally, the assessment addresses the evolving position of synthetic intelligence and gadget learning within cloud environments. The problematic interaction among protection, privacy, and compliance inside the cloud is scrutinized, offering nuanced information on the measures necessary to strengthen those important aspects. Moreover, the paper discusses the impact of cloud computing on numerous industries, from healthcare to finance, and the societal implications of its tremendous adoption. By examining current tendencies and forecasting future trajectories, this evaluates objectivesto guide researchers, practitioners, and selection-makers in navigating the dynamic landscape of cloud computing and harnessing its full capacity.
Cloud Computing Security Challenges and Solution

Cloud computing has become a fundamental part of present-day IT infrastructure, presenting unprecedented tiers of scalability, flexibility, and value efficiency. However, this paradigm shift in computing comes with its very own set of protection demanding situations that demand meticulous examination. This evaluation paper delves into the multifaceted panorama of cloud computing security challenges and explores modern solutions to mitigate these concerns. The primary focus areas encompass records safety, get admission to manipulate, compliance and prison concerns, community safety, virtualization demanding situations, and incident reaction. For each task, the paper evaluates existing vulnerabilities and ability threats, providing a comprehensive overview of the tricky security landscape inside cloud environments. Furthermore, the overview discusses rising technology and developments that keep promise in bolstering cloud safety, together with blockchain for statistics integrity, the Zero Trust Security version for non-stop verification, and cloud-native protection answers. Realinternational case research is analysed to offer practical insights into how organizations have efficiently navigated and mitigated protection demanding situations within the cloud. &#x0D; In conclusion, the overview synthesizes the key findings, underscoring the dynamic nature of cloud security and the need for ongoing research and variation to counter evolving threats. The paper serves as a treasured resource for researchers, practitioners, and decision-makers, imparting a comprehensive information of the current kingdom of cloud computing safety demanding situations and the modern answers shaping the future of stable cloud adoption.
Bias in Machine Learning Algorithms

Bias in machine learning algorithms has emerged as a critical concern, casting a shadow on the perceived objectivity and fairness of these systems. This paper delves into the multifaceted landscape of biases inherent in machine learning models, exploring their origins, manifestations, implications, and potential remedies. The investigation begins by elucidating the sources of bias, stemming from various stages of the machine learning pipeline, including data collection, feature selection, algorithmic design, and human interventions. It unravels how biases, whether implicit in historical data or inadvertently introduced, can perpetuate societal inequalities, reinforce stereotypes, and result in discriminatory outcomes. The paper examines the manifestations ofbias in different domains, such as healthcare, criminal justice, finance, and employment, where machine learning algorithms wield substantial influence. It highlights instances where biased models can lead to unequal treatment, exacerbating societal disparities and compromising ethical standards. Moreover, the study explores the challenges associated with detecting, measuring, and mitigating bias in machine learning algorithms. It navigates through various fairness metrics, algorithmic transparency techniques, and debiasing strategies aimed at promoting fairness, accountability,and transparency in algorithmic decision-making. In addition to uncovering the intricacies of bias, this paper underscores the ethical imperatives in mitigating bias, emphasizing the need for interdisciplinary collaboration,ethical guidelines, and regulatory frameworks. It advocates for a holistic approach that amalgamates technical advancements with ethical considerations to steer machine learning algorithms toward equitable and socially responsible outcomes.In conclusion, bias in machine learning algorithms represents a multifaceted challenge, necessitating a concerted effort from researchers, policymakers, and practitioners. Addressing bias requires not only technical innovations but also ethical scrutiny, transparency, and a commitment to promoting fairness and inclusivity in algorithmic systems.This abstract provides an overview of the multifaceted nature of bias in machine learning algorithms, exploring its origins, implications, challenges, and the necessity for a holistic approach encompassing technical and ethical considerations.
INVESTIGATING THE EFFECTIVENESS OF BANGER RIVER FLOODWAY ON REDUCING WATER LEVEL OF LOJI RIVER IN PEKALONGAN CITY USING HEC-RAS 1D MODEL

Pekalongan City is a lowland area and located in the northern part of Java Island. Tidal flooding often occurred at high tide when the wave run-up entered the mainland and inundated several areas in the city of Pekalongan. The inundation area in Pekalongan City reached 618 Ha in 2020. Tidal flooding became a problem because it inundated vital areas such as residential areas, public spaces, and economic centers. The area that often experiences inundation is in the area of the Loji River and the Banger River estuarine, which is a unified system on the Pekalongan River. The inundation due to tidal flooding will be more severe if it coincided with the occurrence of high discharge in the river flow. To control floods and tidal floods in the city of Pekalongan, flood control engineering was needed so a floodway known as the Banger River was built. This approach aims to reduce the area of inundation and protect existing vital areas. This study aims to assess the impact of the Banger River on reducing water level in the Loji River. The methodology used in this study is flood modeling with HEC-RAS 6.1 one dimensional model. The simulation results show that after the Banger river floodway was made, the water level in the Loji river 1.7 meters or 28% of the water level in the Loji River can be reduced.
Digital Didactic Tools: Conceptual Justification, Structure, Application Algorithm

The authors discuss their fundamental idea for developing a digital didactic toolkit and its application for integrating the digital research and pedagogical platform with the digital educational practice platform. They establish the framework for reflecting scientific and pedagogical research findings in digital educational practice and create a model of interaction between the agents of the digital educational practice platform and the digital research and pedagogical platform.&#x0D; The article discusses the content and functionality of the aforementioned platforms. The digital didactic toolkit is presented as a scientific concept. The study also provides the fundamental ideas of data utilization, such as data-driven research and data-driven management.&#x0D; The offered digital educational process architecture is based on cultural and situational approaches and guarantees absorption of all key educational elements. The digital didactic toolkit is regarded as a technique of developing novel teaching practices.
Transformer-based Deep Embedding Network for Scene Graph Generation

Due to the complexity and variability of the relationships between objects, it becomes very difficult to detect the relationships between them. Scene graph generation (SGG) has been receiving attention as a challenging task in computer vision. Most of the existing scene graph generation methods use two-stage or point-based single-stage methods, but these methods usually suffer from excessive time complexity or poor design assumptions. In this paper, we adopt a single-stage generation method inspired by the transformer. In this, the main body still uses Convolutional Neural Network (CNN) for image feature extraction, and then the extracted features are given to the transformer decoder for encoding and decoding, and then processed to obtain the scene graph. The work in this paper lies in 1) adding a predicate generator to the traditional transformer decoder, and 2) evaluating it on some improved visual genome-based datasets, and the results show that the method improves the SGG's relationship recognition ability.
Research Applications of Hidden Markov Models in Speech Recognition

Reinforcement Learning, a vital branch of Machine Learning, has gained significant attention due to its interactive and goal-oriented learning approach. Its primary objective is to discover the optimal strategy within a continuous time series, thereby enhancing performance and efficiency. In the realm of speech recognition, accurate recognition and prediction of subsequent content have always been pivotal in Artificial Intelligence research. This article centres on the application of advanced Hidden Markov Models in Reinforcement Learning, with particular emphasis on their efficacy in complex speech recognition tasks. By refining the model, researchers delve into the utility of intelligent recognition algorithms in speech recognition contexts. Through a series of meticulous experiments, it was ascertained that the enhanced algorithm demonstrates superior performance in speech recognition compared to its predecessors. This means that by introducing an improved hidden Markov model, the accuracy and predictive ability of speech recognition systems can be improved. The experimental results show that the improved algorithm performs better in speech recognition, providing new ideas for improving system performance and prediction ability.
Characteristics and Educational Support Needs by Life Cycle of Learners with Borderline Intellectual Functioning: Focusing on Gyeonggi-do

In this study, we examined the perceptions of parties, parents, teachers, and experts regarding&#x0D; characteristics and educational support needs by life cycle of learners with borderline intellectual&#x0D; functioning, focusing on Gyeonggi-do. A total of 52 people were selected as research participants,&#x0D; including 6 adolescents with borderline intellectual function, 6 young adults, 8 parents, 13 teachers,&#x0D; and 19 relationship experts, and in-depth interviews were conducted. As a result of analyzing the&#x0D; interview data through continuous comparative analysis, the interview results were divided into 16&#x0D; superordinate categories and 38 categories centered on two main themes such as characteristics&#x0D; and educational support needs of learners with borderline intellectual functioning. Regarding&#x0D; subcategories, 82 contents were derived. Learners with borderline intellectual functioning who&#x0D; participated in the study were experiencing difficulties in learning and social adaptation throughout&#x0D; their entire life cycle, from preschool to adulthood, and related experts said that life cycle-specific&#x0D; support at the national level was needed for these learners. Based on the research results,&#x0D; implications on the direction of support for each life cycle were presented.
RAM-CG: Relation-aware Adaptive Model for Continual Graph Learning

Neural networks excel at mining the static graph, but how to learn from streaming graphs without forgetting previous knowledge is an emerging challenge and well known as continual graph learning (CGL). Despite recent progress in this area, two significant challenges persist: 1) most of existing works only manipulate on the intermediate graph embedding and ignore intrinsic properties of graphs, which limits the learning method’s pertinence. 2) recent attempts obscure the transferable knowledge and lack explicit description for the transfer process. It is non-trivial to tell what and how the information is transferred across graphs. In this paper, we point out that latent relations behind graph edges, as an intrinsic graph property, can be attributed as an invariant factor for the evolving graph sequence and lead a possible knowledge transfer. Motivated by this, we design a relation-aware adaptive model, dubbed as RAM-CG, which consists of a relation analysis module to explore latent relations behind edges for message-passing and a task-awareness masking classifier to account for the shifting knowledge in the graph sequence. Extensive experiments show that RAM-CG provides significant 2.2%, 4.2% and 6.6% accuracy improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and TWITCH dataset.
Promising Areas of Didactic Research in Russia

The article presents the findings of the study undertaken as part of the project for the Russian Foundation for Basic Research — Promising Areas of Didactic Research in Russia. The authors discuss social and cultural issues in didactics, such as instability and non-standard living and professional conditions, the dynamism of the professional arena, unexpected intricacy of job responsibilities, and others. The study reveals how the research area has expanded beyond the discovery of common patterns in schooling and higher education to include preschool, postgraduate, corporate, domestic, and senior citizen education, as well as tutoring. It raises new didactic challenges related to the digital transformation of education and describes changes in the methodological regulation of didactic research using V.S. Styopin’s typology of scientific rationality, which encompasses classical, non-classical, and post-nonclassical kinds.&#x0D; The authors present a review of didactic research in higher education based on the integration of competency and value-semantic methods that focus on the search and application of perso­nal meanings in both education and subsequent activities. They also discuss the specific features of adult education practices as well as the challenges they pose to didactics, including changes in subjects of education, multi-agency in the educational process, multiple ‘integration points,’ ‘non-cognitive’ types of experience, a shift in the teacher’s position, and the coherence and complexity of didactic theory.