Reviewerly/exHarmony huggingface dataset:
BAKTI SOSIAL BERSAMA JAMAAH MASJID JAMI NURUL YAQIN

Jami Nurul Yaqin Mosque is one of the mosques located in Cipelang district, Bogor that not only serves as a place of worship, but also has a role as a means of community interaction. Various activities can be done in the mosque, ranging from routine worship activities such as obligatory prayers, Jum'ah prayers to educational activities such as Ta'lim and equipped with facilities Education of children (Madrasah). But often the role of the mosque is not functioning optimally due to the availability of facilities and infrastructure. Therefore, the devotion activities focused on the procurement and optimization of facilities and infrastructure of Masjid Jami Nurul Yaqin include the procurement of mukena, cleaning equipment, and decoration of the madrasah study room. In addition, in the implementation of devotion also occurred interaction with the board of DKM and students of Madrasah Fatmah Hidayah. Interaction includes socialization activities and participate in teaching and learning activities. Steps taken in devotion include: (1) Provision of support facilities, (2) Optimization and maintenance of existing facilities, and (3) Interaction through involvement in the assembly.
Reading: An Active Language Learning Skill

This article presents on some perspectives on reading. It considers reading as a robust resource and skill for learning. Reading is one of the major avenues of communication. It has occupied a good space at the Nepalese educational system. This is a review article where an in-depth information and insight has been reviewed. The techniques that are used in this study are document review and theoretical knowledge. Relevant literature is reviewed to make the study conceptually and theoretically reliable and authentic. The study shows that to read is to lead. The desire to write comes from reading. It demands both passion and compassion to be a voracious reader. It is not only receptive but an active, communicative and interactive skill. It leads to understanding, communication, capacity building, critical thinking, testing and assessment. It is the means by which every age is linked to every other. It makes possible reader’s capacity for time binding, the ability to perceive himself or herself in the historic process and the fluid universe around him or her. Teaching reading is a humanizing process. Reading involves skills and thinking. It affects the entire personality. Through reading the learner can view ever-widening horizons and explore ever-new areas in the world of things, people and events. The key to learning, therefore, is the cultivation not only of the ability to read, but also of the love of reading.
An implementation of secure storage using blockchain technology on cloud environment

Data generation and consumption have significantly increased recently, necessitating the need for secure and dependable file storagesolutions. The vulnerability of current centralized storage solutions to data breaches and hackers compromises the security andintegrity of user data. These problems may have a workable solution in a decentralized file storage system. In order to offer a secureand dependable storage solution, this paper proposes a blockchain-based file storage (BBFS) system that takes advantage of featureslike immutability, transparency, and security. Any user can upload unlimited files (one at a time) with this proposed system. Users candownload and access those files on their machines as well as all other peers. As soon as a peer uploads a file, it is placed in a blockalong with the user name, file size, and file information. It is not possible to change or remove these blocks because they are addedto the current blockchain. These blocks can be connected with cloud storage, giving users a safe place to store and access their filesthat cannot be altered. By integrating this proposed system with cloud storage, customers can take advantage of the scalability andsecurity of cloud services as well as the immutability and security of blockchain. The proposed system addresses the cost and scalability roblems that make to be widely applicable.
Examining the relationship between high school student's post-graduation career plans, college major choices and academic achievement

In this study, we investigated the relationship between high school students’ post-graduation career plans, college major choices and academic achievement. Drawing upon data from the first and second years of the Korean Education and Employment Panel II, we explored the relationship between students' post-graduation career plans, their choice of college major during the second year of high school, and their academic achievement in the third grade utilizing multiple regression approaches.&#x0D; The findings revealed a positive correlation between college aspiration during the second year of high school and higher academic achievement in the third year of high school. Students who set specific goals for college admission demonstrated a better academic performance compared to those who did not. Conversely, no statistically significant relationship is observed between college major choices during the second year of high school and their academic achievement in the third 3rd year of high school.&#x0D; Based on these results, it is confirmed that the aspiration to pursue college among high school students is positively related to academic achievement, serving as a motivating factor for individuals. Therefore, the findings of this study underscores the significance of goal-setting during the high school years, thereby the importance of career education and counseling. The findings of this research also have policy implications, highlighting the need to prioritize goal-setting among high school students and provide adequate support for students’ career development.
Implementing Augmented Reality in Inclusive Education: Experiments and Potential

Technological developments often support the development of learning methods, where currently the implementation of e-learning is quite common. Do not miss inclusive school Students can be helped in their education through the use of this technology, namely the use of Augmented Reality. This study aims to look at the implementation and potential that can be provided by the presence of Augmented Reality for inclusive education. This research was conducted using a descriptive qualitative approach. The data used in this study were obtained from the library study method. The results of this study then found that Augmented Reality cannot be implemented in general as a learning medium in schools like other e-learning media. However, the use of Augmented Reality can help educators to provide knowledge for students. In the case of inclusive education, generally, the presence of Augmented Reality has a positive impact and is liked by students. This shows that the existence of Augmented Reality can be useful as a learning medium for inclusive education.
Data-Parallel Primitives for Minimizing Many-core Development Cost.

these goals. For development time, we study 57 algorithms implemented in the VTK-m software library and determine that MCD3 leads to significant savings. For efficient performance, we survey ten studies looking at individual algorithms and determine that the MCD3 hardware-agnostic approach leads to performance comparable to hardware-specific approaches: sometimes better, sometimes worse, and better in the aggregate. In total, we find that MCD3 is an effective approach for scientific visualization libraries to support many-core architectures.
Applying digital transformation in UEF syllabus of human resource management

Industry 4.0, especially the application of digital transformation, changes the nature and operation of many organizations. It makes many companies change to adapt and keep up with the new trend in the digital age. The field of higher education is no exception to the general trend of applying digital transformation. Although digital transformation plays an important role for universities, there are still not many previous studies about applying digital transformation in lecturers’ teaching activities, specifically in the eduaction program of Human Resource Management major. Because of understanding the importance of applying digital transformation in higher education and the research rationale about applying the digital transformation in education programme of Human resource management major, the authors decide to choose the topic “Solutions for applying the digital transformation to improve the quality of teaching activities of lecturers in the education programme of Human management management major at the University of Economics and Finance”. Based on the research, the authors identify the strengths and limitations existing in the teaching methods of the education program of the Human Resources Management major at UEF, thereby proposing some recommendations to develop this major in the future.
PENGEMBANGAN MEDIA PEMBELAJARAN VIDEO ANIMASI KESELAMATAN KEBAKARAN PADA MATA PELAJARAN PENDIDIKAN JASMANI KELAS IX SMP BPS&amp;K JAKARTA TIMUR

Until now, fire disasters cannot be avoided, both in the industrial sector, housing and even in the forest. From various types of fire cases, it is very detrimental if there are casualties, so there is a need for education about fire safety learning for adults, teenagers and even children. Children's understanding of danger and self- protection causes children to be less able to anticipate and overcome dangerous conditions around themselves. This can usually be fatal for the safety of the child. Even when there is no adult supervision around the children, they cannot optimally look after and supervise the children. The results of the development of learning media for the feasibility of animated videos that cause fires and how to prevent fires, according to material experts, are categorized as feasible with a value of 76% measured by several aspects, namely: material coverage, presentation clarity, language aspects, understanding. Meanwhile, the feasibility of animated videos that cause fires and how to prevent fires, according to media experts, is categorized as very feasible with a score of 84% measured by several aspects, namely: text, images, video, audio, program effectiveness. And the feasibility of animated videos that cause fires and how to prevent fires based on the assessment of junior high school students is categorized as very feasible with a score of 88% measured by several aspects, namely: text, images, video, audio, program effectiveness.
Fostering Collaborative Learning through Presentation, Practice, and Production: Innovative Techniques for Teaching Grammar

In the method of Foreign Language Teaching, Presentation-Practice-Production (PPP) is used to teach language structures (grammar). The presentation phase is controlled by the lecturer by asking students to make presentations in groups. During the practice phase, learners practice speaking or writing the language structure correctly. Practice activities include drills, multiple-choice exercises, gap-fill exercises, and cue exercises. In this phase, the lecturer's role is to provide positive feedback to students, correct mistakes, and model the correct forms. In the production phase, learners have fully mastered the form and have learned how to produce oral and written texts. This research aims to develop a grammar teaching strategy in Collaborative Learning by employing PPP and some alternative techniques for fourth-semester students in the English Department at Universitas Nahdlatul Ulama Sidoarjo. The study employs action research with two cycles. The research findings show an improvement in students' average grammar scores, from 46.22 in the preliminary study to 71 in the first cycle and 81 in the second cycle. Furthermore, the results of the study indicate that students were enthusiastic and active in the teaching and learning process.
Enhancing Reading Instruction for Second Semester Students at Universitas Nahdlatul Ulama Sidoarjo Using Graphic Organizers

The aim of this research is to describe the process, advantages, and disadvantages of implementing Graphic Organizers (GO) in teaching reading. This study is of a descriptive qualitative nature and was conducted in the English Teaching Department of Universitas Nahdlatul Ulama Sidoarjo. Class PBI 2021 was selected as the sample using the purposive sampling technique. Data collection was carried out through observation and interview methods. Data analysis involved the steps of data classification, interpretation, and conclusion. The results of this research indicate that the application of GO consists of three steps: pre-reading, while reading, and post-reading. The advantages include making reading instruction more effective, fostering cooperative learning environments, enhancing students' confidence and motivation in reading classes, and promoting an active and engaging classroom atmosphere. However, some disadvantages were noted, such as the strategy's limited applicability to certain texts and the potential for increased noise levels in the classroom. It is recommended for lecturers to incorporate this strategy into their reading instruction.
DIMENSIONES CULTURALES DE LOS EFECTOS DE LA PANDEMIA DE LA COVID-19 EN DOCENTES UNIVERSITARIOS EN CENTROS EDUCATIVOS DE COLOMBIA Y MÉXICO

The sudden change generated at the time of the context of the Covid -19 pandemic, in university teachers led them to experience changes as a result of confinement where it implied the development of their academic work from another context and making use of other forms and strategies. educational technologies. Objective: To analyze the cultural dimensions of the effects of the COVID-19 pandemic on university teachers from three educational centers in Colombia and one in Mexico. Methodology: study design: cognitive cultural anthro- pological, focuses on analyzing how people from different cultures acquire information about the world, how they process it, make decisions and act in ways considered appropriate for other members of their culture (cultural transmission). It is developed in four university centers in Colombia and Mexico, 160 teachers are studied in the first phase of the study through free listings through an open online survey. Findings: the term work overload was included in the four participating sites, with the greatest weight in Bogotá and Cali (Colombia). And in the personal, the presence of COVID-19 itself, negative emotional states, and economic-so- cial effects resulting from prevention measures were highlighted. Also, in complex processes of adaptation, learning and transformation in the use of technological teaching media. Conclusions: the confinement, the uncertainty of the disease, the presence in their families and the changes in the work-family context generated an impact on their professional, personal, economic and social spheres in the teachers of the studied university centers in Colombia and Mexico.
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms

Machine learning (ML) models that achieve high average accuracy can still underperform on semantically coherent subsets ("slices") of data. This behavior can have significant societal consequences for the safety or bias of the model in deployment, but identifying these underperforming slices can be difficult in practice, especially in domains where practitioners lack access to group annotations to define coherent subsets of their data. Motivated by these challenges, ML researchers have developed new slice discovery algorithms that aim to group together coherent and high-error subsets of data. However, there has been little evaluation focused on whether these tools help humans form correct hypotheses about where (for which groups) their model underperforms. We conduct a controlled user study (N = 15) where we show 40 slices output by two state-of-the-art slice discovery algorithms to users, and ask them to form hypotheses about an object detection model. Our results provide positive evidence that these tools provide some benefit over a naive baseline, and also shed light on challenges faced by users during the hypothesis formation step. We conclude by discussing design opportunities for ML and HCI researchers. Our findings point to the importance of centering users when creating and evaluating new tools for slice discovery.
Informing Users about Data Imputation: Exploring the Design Space for Dealing With Non-Responses

Machine learning algorithms often require quantitative ratings from users to effectively predict helpful content. When these ratings are unavailable, systems make implicit assumptions or imputations to fill in the missing information; however, users are generally kept unaware of these processes. In our work, we explore ways of informing the users about system imputations, and experiment with imputed ratings and various explanations required by users to correct imputations. We investigate these approaches through the deployment of a text messaging probe to 26 participants to help them manage psychological wellbeing. We provide quantitative results to report users' reactions to correct vs incorrect imputations and potential risks of biasing their ratings. Using semi-structured interviews with participants, we characterize the potential trade-offs regarding user autonomy, and draw insights about alternative ways of involving users in the imputation process. Our findings provide useful directions for future research on communicating system imputation and interpreting user non-responses.
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity

Hybrid human-ML systems increasingly make consequential decisions in a wide range of domains. These systems are often introduced with the expectation that the combined human-ML system will achieve complementary performance, that is, the combined decision-making system will be an improvement compared with either decision-making agent in isolation. However, empirical results have been mixed, and existing research rarely articulates the sources and mechanisms by which complementary performance is expected to arise. Our goal in this work is to provide conceptual tools to advance the way researchers reason and communicate about human-ML complementarity. Drawing upon prior literature in human psychology, machine learning, and human-computer interaction, we propose a taxonomy characterizing distinct ways in which human and ML-based decision-making can differ. In doing so, we conceptually map potential mechanisms by which combining human and ML decision-making may yield complementary performance, developing a language for the research community to reason about design of hybrid systems in any decision-making domain. To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity. Through synthetic simulations, we demonstrate how this framework can be used to explore specific aspects of our taxonomy and shed light on the optimal mechanisms for combining human-ML judgments.
How Crowd Worker Factors Influence Subjective Annotations: A Study of Tagging Misogynistic Hate Speech in Tweets

Crowdsourced annotation is vital to both collecting labelled data to train and test automated content moderation systems and to support human-in-the-loop review of system decisions. However, annotation tasks such as judging hate speech are subjective and thus highly sensitive to biases stemming from annotator beliefs, characteristics and demographics. We conduct two crowdsourcing studies on Mechanical Turk to examine annotator bias in labelling sexist and misogynistic hate speech. Results from 109 annotators show that annotator political inclination, moral integrity, personality traits, and sexist attitudes significantly impact annotation accuracy and the tendency to tag content as hate speech. In addition, semi-structured interviews with nine crowd workers provide further insights regarding the influence of subjectivity on annotations. In exploring how workers interpret a task — shaped by complex negotiations between platform structures, task instructions, subjective motivations, and external contextual factors — we see annotations not only impacted by worker factors but also simultaneously shaped by the structures under which they labour.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts. However, this progress poses security and privacy concerns, necessitating effective solutions for distinguishing deepfake texts from human-written ones. Although prior works studied humans’ ability to detect deepfake texts, none has examined whether “collaboration” among humans improves the detection of deepfake texts. In this study, to address this gap of understanding on deepfake texts, we conducted experiments with two groups: (1) nonexpert individuals from the AMT platform and (2) writing experts from the Upwork platform. The results demonstrate that collaboration among humans can potentially improve the detection of deepfake texts for both groups, increasing detection accuracies by 6.36% for non-experts and 12.76% for experts, respectively, compared to individuals’ detection accuracies. We further analyze the explanations that humans used for detecting a piece of text as deepfake text, and find that the strongest indicator of deepfake texts is their lack of coherence and consistency. Our study provides useful insights for future tools and framework designs to facilitate the collaborative human detection of deepfake texts. The experiment datasets and AMT implementations are available at: https://github.com/huashen218/llm-deepfake-human-study.git
A Hybrid Model with New Word Weighting for Fast Filtering Spam Short Texts

Short message services (SMS), microblogging tools, instant message apps, and commercial websites produce numerous short text messages every day. These short text messages are usually guaranteed to reach mass audience with low cost. Spammers take advantage of short texts by sending bulk malicious or unwanted messages. Short texts are difficult to classify because of their shortness, sparsity, rapidness, and informal writing. The effectiveness of the hidden Markov model (HMM) for short text classification has been illustrated in our previous study. However, the HMM has limited capability to handle new words, which are mostly generated by informal writing. In this paper, a hybrid model is proposed to address the informal writing issue by weighting new words for fast short text filtering with high accuracy. The hybrid model consists of an artificial neural network (ANN) and an HMM, which are used for new word weighting and spam filtering, respectively. The weight of a new word is calculated based on the weights of its neighbor, along with the spam and ham (i.e., not spam) probabilities of short text message predicted by the ANN. Performance evaluations on benchmark datasets, including the SMS message data maintained by University of California, Irvine; the movie reviews, and the customer reviews are conducted. The hybrid model operates at a significantly higher speed than deep learning models. The experiment results show that the proposed hybrid model outperforms other prominent machine learning algorithms, achieving a good balance between filtering throughput and accuracy.
Token Selection from Multiple Input Places

This chapter discusses the selection of tokens from multiple input places, as the previous two chapters dealt with selecting tokens from a single input place. The following Sects. 4.1 and 4.2 explain how we can let a transition consume tokens (based on colors) from different input places.