Reviewerly/exHarmony huggingface dataset:
Improving Students’ Vocabulary Mastery Through Folktale Storises at SMP Al-Azhar Mandiri Palu

The development of vocabulary mastery is an important goal of teaching English. One of the approaches that are effective in fostering the vocabulary mastery is using folktale stories. The objective of this research is to prove that the use of folktale stories can improve the vocabulary mastery of the first year students of SMP Al-Azhar Mandiri Palu. The sample was taken through random sampling technique. The data were collected by using two kinds of instrument: observation and test. The data observation was analyzed descriptively, whereas the data of the test was analyzed by using statically analysis. After conducting the observation and test, the writers found that the students have good motivation in learning and the result of the data analysis showed that there was significance difference between pre-test and post-test. It is revealed by the hypothesis of the research stated that the vocabulary mastery of the first year student of SMP Al-Azhar Mandiri Palu can be improved by applying folktale stories. This hypothesis was accepted after testing it, in which t-counted value 4.18. By applying the 0, 05 level of significance and 72 degree of freedom with the critical value of the t-table through interpolation computation is 1.996. The result of t-counted value is greater than t-table values. It means that the alternative hypothesis (Ha) is accepted and the Null hypothesis (Ho) is rejected. The writers concludes that the use of folktale stories can improve the vocabulary mastery of the first year student of SMP Al-Azhar Mandiri Palu.
Research on Library Collection Optimization Based on Collection Structure and Literature Utilization Ratio—A Case Study of Nanhu Branch Library of Central China Normal University

In this paper, descriptive statistics method is adopted to make a statistical analysis of the collection data of Nanhu Branch Library of Central China Normal University from 2021 to 2022. According to faculties in Nanhu Campus, the demands of readers, as well as the utilization rate of library literatures in Nanhu Branch Library, this paper intends to put forward some suggestions on library collection optimization to maximize the utilization rate of library literatures.
Improving Students' Ability in Speaking Through Role Play Technique

Role-play in learning leads to a teaching-learning activity more focused on students. This research aimed to investigate the improvement of the speaking aspect in the classroom. This research was Classroom Action Research (CAR) in two cycles for 25 eighth-grade students of SMPN 1 Serawai in the academic year 2014–2015. The results showed that the mean fluency score increased from 24.20 in the first cycle to 28.88 in the second cycle. This shows that speaking is sometimes hesitant. The pronunciation has risen from 24.20 to 30.60, which indicates student’s pronunciation is in the category of not foreign accents and occasional mispronunciation but does not interfere with the understanding. The grammar component was 9.76, increasing to 13.88, indicating that the student’s grammar has occasional errors showing interference control and some pattern but no weakness that causes misunderstanding. The result of the observation and the student assessment also showed that there were improvements in the student's participation during teaching and learning activities. The researcher suggests that teachers can use this technique to help students improve their speaking and other English skills.
Enhancing English Language Proficiency: Strategies For Improving Student Skills

English language proficiency is a critical skill for academic success, particularly for culturally and linguistically diverse students. This article aims to provide educators with effective strategies to assist these students in developing the literacy skills necessary for academic success. The problem is that English language learners (ELLs) have particular educational needs that require specialized teaching approaches. The purpose of this article is to provide educators with effective strategies to assist ELLs in developing literacy skills crucial for academic success. The Qualitative method used is a review of literature, including theories of language acquisition and learning, differences in social and academic language, and effective English language learning strategies employed by successful language learners. The participants of this study were Thirty Students in one of the senior high schools in Lombok Timur. The results of this review include six essential strategies for teaching ELLs, including cultivating relationships and being culturally responsive, teaching language skills across the curriculum, using real objects to illustrate concepts, role-playing or pantomiming, pointing to pictures, and employing total physical response.
Powered Dirichlet Process - Controlling the “Rich-Get-Richer” Assumption in Bayesian Clustering

The Dirichlet process is one of the most widely used priors in Bayesian clustering. This process allows for a nonparametric estimation of the number of clusters when partitioning datasets. The “rich-get-richer” property is a key feature of this process, and transcribes that the a priori probability for a cluster to get selected dependent linearly on its population. In this paper, we show that such hypothesis is not necessarily optimal. We derive the Powered Dirichlet Process as a generalization of the Dirichlet-Multinomial distribution as an answer to this problem. We then derive some of its fundamental properties (expected number of clusters, convergence). Unlike state-of-the-art efforts in this direction, this new formulation allows for direct control of the importance of the “rich-get-richer” prior. We confront our proposition to several simulated and real-world datasets, and confirm that our formulation allows for significantly better results in both cases.
Pengenalan dan Pelatihan Global Biodiversity Information Facility pada Mahasiswa Program Studi Pendidikan Biologi Universitas Pendidikan Mandalika

The service activity aims to introduce the importance of biodiversity data and train participants (Students of the Department of Biology Education) how to process data using the website from the Global Biodiversity Information Facility (GBIF) as a data source. This is done so that students are no longer confused in finding research data and looking for references on biodiversity data. This activity is intended for Department of Biology Education students who will take a thesis. The service implementation method consists of several stages, including: coordinating activities, implementing activities (introduction to GBIF), and training using the GBIF site. The service activity has been carried out for 2 days, namely Monday-Tuesday, 2-3 November 2020. The activity is carried out through zoom media due to the Covid-19 Pandemic condition. The activity was attended by 18 students from the Department of Biology Education, FSTT, Mandalika University of Education, semester 7, as many as 18 students. The first day of activities, November 2, 2020 at 14.00 WITA, began with the introduction of GBIF to students of the Department of Biology Education, FSTT, Mandalika University of Education. Then on the second day on November 3 at 09.00 WITA, the Service Team provided training on the use of GBIF and provided examples of thesis titles that could be done using the GBIF website. GBIF was first introduced to students of the Department of Biology Education, FSTT, Mandalika University of Education, and students were very enthusiastic to know that there was a site that could help them in finding research data and looking for references on biodiversity data to compose their thesis.
Satisfaction in using barcode attendance for the Criminology Students of Data Center College of the Philippines, Laoag City

This study determined the satisfaction in using barcode attendance for the Criminology Students of Data Center College of the Philippines. The study utilized the quantitative method of research using survey questionnaires to gather data from the 200 criminology students of Data Center College of the Philippines of Laoag City, Inc.-College of Criminal Justice Education. Mean was used to analyze the data gathered in level of satisfaction, problem encountered and coping mechanism of the study. The study revealed that most of the average responses tend to be in satisfaction range (4.20 – 3.41) with the corresponding statement in the survey questionnaire. In the problem encountered that they experience, most of the items fall in the range of (3.40 – 2.61) that interpreted as sometimes. However, those respondents who encountered those problems were able to cope up through problem-solving and problem-focused. In addition, the result of the study also showed that majority of problems are in the part of scanner. Meaning in order to improve the system let us focus first in the scanner because this is the first component to process that if it is not working well the whole system affected. Based on the findings and conclusions drawn the following are recommended that the Data Center College of the Philippines need to establish a updated and upgraded barcode scanner to have a better performance attendance monitoring in the school. In terms of security of the students the SMS parent notification is recommended for the parental monitoring to avoid absenteeism and parent appearance in the guidance. Also concern in portability of the system, it is highly recommended that there is an app installed to mobile phones that connected to the system to easily monitor the attendance of the student during activities which are not happened inside the school.
Efficient Vocabulary Memory: Taking the Vocabulary Question Type of the Test for English Majors-Band 4 as an Example

With the development of globalization, English has become an indispensable language in international communication and business activities. Therefore, English exams have become one of the essential skills in various fields. This article took the vocabulary question type of the Test for English Majors-Band 4 as an example to explore methods for efficient vocabulary memory. Through empirical research, this article identified some effective vocabulary memory methods and proposed some improvement suggestions. This article adopted experimental control and investigation methods to compare and analyze three memory methods. The experimental results showed that students who used the flashcards word memory method had a maximum accurate vocabulary count of 90. With the help of Flashcards memory method, the experimental group achieved higher memory efficiency, deeper memory depth, and stronger memory persistence.
Research on the Exploration of the Future Trends of Online Teaching Evaluation based on the Perspective of the Metaverse

Since the outbreak of the COVID-19 pandemic in early 2020, the education sector has been operating under a special teaching context. Online teaching has become a common practice for dealing with cross-regional, clustered learning, and communication scenarios. Various aspects of online teaching have gradually been improved as online teaching activities continue to unfold. However, the online teaching evaluation process, which assesses the behavior and performance of different participants and aspects in online teaching activities, still has many shortcomings. It is difficult to conduct a comprehensive and objective evaluation of online teaching activities. Metaverse technology, which integrates artificial intelligence, big data, and other technologies, is expected to have a long-term impact on online teaching. Utilizing metaverse technology for online teaching evaluation can overcome the obstacles faced in current online teaching evaluation and promote the achievement of teaching objectives and the development of teaching participants. This article analyzes the problems and causes existing in online teaching evaluation from a metaverse perspective, highlights the advantages of applying metaverse technology to online teaching evaluation, and indicates the intelligent, diverse, and open development trend of future online teaching evaluation. This trend will truly impact every individual involved in the era of high-quality education.
An event-driven and lightweight proactive auto-scaling architecture for cloud applications

The cloud environment is used by the application providers (APs) to host their applications in order to reduce procurement and management costs of the cloud resources. Moreover, the variation in the traffic load of the client applications and the appealing auto-scaling capability of the cloud resources have prompted application providers to seek ways to reduce the cost of their rented services. This paper describes a constructive auto-scaling mechanism based on the events in cloud systems fitted with heuristic predictors. The predictor examines historical data using these approaches: (1) Double Exponential Smoothing (DES), (2) Triple Exponential Smoothing (TES), (3) Weighted Moving Average (WMA) and (4) WMA with Fibonacci numbers. The outcomes of this model simulation in CloudSim indicate that the model can decrease the application provider's cost while preserving application user satisfaction.
An Analysis of the Impact of Metaverse on Climate Change

The metaverse is the next evolution of the Internet, merging the physical and digital worlds into a multiuser environment (Jauhiainen et al. in Sustainability 15(1):346, 2023). It enables interaction with digital objects, virtual environments, and people in 3D settings in real-time using multiple senses. The metaverse is expected to affect various aspects of society, including work and leisure. However, discussions about the metaverse mainly revolve around speculation about its emergence, potential impacts, and effects on climate change and the environment. This paper aims to examine the impacts of the metaverse on climate change in detail, including how it may assist in combating climate change and its potential negative effects. Additionally, this paper also explores how new technologies like the metaverse may contribute to carbon emissions, and whether they may increase or decrease emissions and their impact on climate change.
Is My Neural Net Driven by the MDL Principle?

The Minimum Description Length principle (MDL) is a formalization of Occam’s razor for model selection, which states that a good model is one that can losslessly compress the data while including the cost of describing the model itself. While MDL can naturally express the behavior of certain models such as autoencoders (that inherently compress data) most representation learning techniques do not rely on such models. Instead, they learn representations by training on general or, for self-supervised learning, pretext tasks. In this paper, we propose a new formulation of the MDL principle that relies on the concept of signal and noise, which are implicitly defined by the learning task at hand. Additionally, we introduce ways to empirically measure the complexity of the learned representations by analyzing the spectra of the point Jacobians. Under certain assumptions, we show that the singular values of the point Jacobians of Neural Networks driven by the MDL principle should follow either a power law or a lognormal distribution. Finally, we conduct experiments to evaluate the behavior of the proposed measure applied to deep neural networks on different datasets, with respect to several types of noise. We observe that the experimental spectral distribution is in agreement with the spectral distribution predicted by our MDL principle, which suggests that neural networks trained with gradient descent on noisy data implicitly abide the MDL principle.
Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot

With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code. GitHub Copilot, also referred to as the “AI Pair Programmer”, has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021. However, little effort has been devoted to understanding the practices, challenges, and expected features of using Copilot in programming for auto-completed source code from the point of view of practitioners. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions. More specifically, we searched and manually collected 303 SO posts and 927 GitHub discussions related to the usage of Copilot. We identified the programming languages, Integrated Development Environments (IDEs), technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot. The results show that when practitioners use Copilot: (1) The major programming languages used with Copilot are JavaScript and Python, (2) the main IDE used with Copilot is Visual Studio Code, (3) the most common used technology with Copilot is Node.js, (4) the leading function implemented by Copilot is data processing, (5) the main purpose of users using Copilot is to help generate code, (6) the significant benefit of using Copilot is useful code generation, (7) the main limitation encountered by practitioners when using Copilot is difficulty of integration, and (8) the most common expected feature is that Copilot can be integrated with more IDEs. Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it. Our study provides empirically grounded foundations that could inform software developers and practitioners, as well as provide a basis for future investigations on the role of Copilot as an AI pair programmer in software development.
Entanglement recovery by weak measurement reversal in tripartite systems

Entanglement protection from noisy environments is an essential task in practical quantum information and communication. In this paper, we investigate the entanglement recovery of an amplitude-damped tripartite GHZ state by a weak-measurement reversal procedure. In particular, we emphasize the key importance of the inequivalency of probability amplitudes of the tripartite system under the recovery technique. We explore the maximal and non-maximal tripartite entangled state scenarios under amplitude damping noise in the absence and presence of weak measurement reversal procedures. Importantly, the non-maximal entangled state turns out to be a good choice for the entanglement recovery via weak-measurement reversal procedure.
The subfield metric and its application to quantum error correction

We introduce a new weight and corresponding metric over finite extension fields for asymmetric error correction. The weight distinguishes between elements from the base field and the ones outside of it, which is motivated by asymmetric quantum codes. We set up the theoretic framework for this weight and metric, including upper and lower bounds, asymptotic behavior of random codes, and we show the existence of an optimal family of codes achieving the Singleton-type upper bound.
New Paths in Small Manufacturing: How is Digital Transformation Shaping Collaboration Between Designers and Craftspeople?

Collaboration between designers and craftspeople, or designers and small manufacturers, is a strategy aiming to revive the local manufacturing industry in countries impacted by post-industrial economic challenges. In this paper we examine a local initiative—a community of practice (CoP) composed of designers and craftspeople—by studying how they have used technology to collaborate, in order to identify opportunities for long-term growth of this community. To gain a deep understanding of the dynamics in the community, the collaboration process during the workshop and after it, we used a three-phase qualitative research strategy. We conducted interviews with the participants and staff, analyzed an 11-month period of communications in the WhatsApp group and, finally, conducted a co-design session. We identify opportunities and challenges related to the design and implementation of a personalized collaborative system that fulfills the needs of the users as individuals and community members.
Constrained-HIDA: Heterogeneous Image Domain Adaptation Guided by Constraints

Supervised deep learning relies heavily on the existence of a huge amount of labelled data, which in many cases is difficult to obtain. Domain adaptation deals with this problem by learning on a labelled dataset and applying that knowledge to another, unlabelled or scarcely labelled dataset, with a related but different probability distribution. Heterogeneous domain adaptation is an especially challenging area where domains lie in different input spaces. These methods are very interesting for the field of remote sensing (and indeed computer vision in general), where a variety of sensors are used, capturing images of different modalities, different spatial and spectral resolutions, and where labelling is a very expensive process. With two heterogeneous domains, however, unsupervised domain adaptation is difficult to perform, and class-flipping is frequent. At least a small amount of labelled data is therefore necessary in the target domain in many cases. This work proposes loosening the label requirement by labelling the target domain with must-link and cannot-link constraints instead of class labels. Our method Constrained-HIDA, based on constraints, contrastive loss, and learning domain invariant features, shows that a significant performance improvement can be achieved by using a very small number of constraints. This demonstrates that a reduced amount of information, in the form of constraints, is as effective as giving class labels. Moreover, this paper shows the benefits of interactive supervision—assigning constraints to the samples from classes that are known to be prone to flipping can further reduce the necessary amount of constraints.
Systematic review on the efficacy of behavioral activation therapy in the management of adult patients with depression

Introduction: This work aimed to assess the approach of behavioral activation for the treatment of adult patients diagnosed with depressive disorder; the importance of this study in the clinical setting is significant since it allowed us to know the viability and acceptability of the treatment. Behavioral activation is the basis of this study. Materials and method: Systematic review, under the PRISMA methodology, the PICO tool was implemented to formulate the research question. Results: 42 articles were located, of which, upon complete reading, 23 met the inclusion criteria established for the review.
Development of Comic Media for Children's Stories Material Characteristics of Poetry in Indonesian Subjects to Increase Students' Interest in Reading

This research is motivated by the limited teaching materials in elementary schools and the lack of development of learning media that can attract students' interest in reading. This development research aims to produce children's story comic products, namely KOMPISA (Indonesian Smart Comics) and find out the practicality and effectiveness of students towards Children's Story Comics in Indonesian Language Subjects in class IV, so that the quality of learning at school is much more enjoyable and is better able to hone their skills. students in increasing interest in reading.. The results of this study, produce Children's Story Comics that can be used at the elementary school level as teaching materials for students who have met the very good criteria with an average score of the three experts, namely 96.9% in the very category. feasible to use, and the practicality level of 87.9% is in the very practical criteria and the level of effectiveness with an average of 90% is in the very effective category. The teaching materials for children's story comics are appropriate, practical and effective for use in learning Indonesian, material for the characteristics of poetry for grade IV elementary schools.