Reviewerly/exHarmony huggingface dataset:
KONSTRUKSI CITRA MUSLIMAH PADA AKUN TIKTOK @putriaqillaramadhani

This research aims to describe and uncover the construction of the Muslimah image on the TikTok account @putriaqillaramadhani. In the digital era and the development of social media, platforms like TikTok have become important for individuals to build identities and influence others' perceptions. Muslim women use social media as a platform to express their religious beliefs, share inspiration, and spread positive messages about Islamic life. This study employs a qualitative approach to understand how the Muslimah image is represented and constructed through the content of the TikTok account. Semiotic analysis is used to analyze video content, descriptions, comments, and interactions with followers. Data are collected through observation and documentation. The results show that the @putriaqillaramadhani account actively constructs a strong and positive Muslimah image using polite and courteous language. Common topics include Islamic teachings, daily Muslimah life, and spiritual advice. The account also utilizes religious symbols such as the hijab and the Qur'an to strengthen the Muslimah identity. Additionally, it promotes positive messages about Muslimah life, such as diversity, tolerance, and empathy, by sharing personal experiences and inspirational stories with followers
Semantic Relatedness and the Efficacy of Retrieval Practice

There is growing interest in the role that semantic memory plays in the retrieval practice (or testing) effect. We investigated how semantic relatedness impacts the efficacy of testing, relative to a restudy control, using a cued-recall paradigm with immediate correct answer feedback. High and low relatedness word pairs were created by selecting two different response words for each cue word based on associative norms. The parameter-free predictions of the dual-memory model of test-enhanced learning for low relatedness pairs were used as an empirically well-supported scaling benchmark for evaluating the effectiveness of testing for high relatedness pairs, allowing us to overcome the issue of removable interactions. Results showed a robust testing effect at both relatedness levels. For the high relatedness group, however, the testing effect was about 26% smaller than expected by the model. Nevertheless, the predicted quadratic relation between test and restudy proportion correct in the cumulative distribution held for both groups. One candidate account of those results within the dual-memory framework is that, for the high relatedness case, study and test memory strengths for tested items are positively correlated rather than independent. A simulation implementing that version of the model provided a good distribution fit for the high relatedness group. Other theoretical accounts of the semantic relatedness effect are considered.
NeVer2: Learning and Verification of Neural Networks

Abstract NeVer2 is a cross-platform open-source tool to design, train and verify neural networks. NeVer2 integrates off-the-shelf learning libraries with our verification backend and makes their functionalities available through a graphical interface. A user can work out the structure of a neural network by dragging and dropping blocks on a canvas and then train the network by supplying a dataset source and the hyper-parameters through dialog boxes. Once the network is trained, verification amounts to (i) adding input pre-conditions and output post-conditions to the network through special blocks and (ii) starting verification in a "push-button" fashion. To the best of our knowledge, there is no other publicly available tool that integrates all such features at present. Here we provide a detailed description of NeVer2 including examples to demonstrate the seamless integration between design, training and verification. We further provide an experimental analysis on several verification benchmarks in order to show the trade-off between completeness and computability using different algorithms, including a comparison with the state-of-the-art tools α,β-CROWN and NNV for reference.
Effects of causal structure and evidential impact on probabilistic reasoning

We compare two perspectives on base-rate neglect (Kahneman &amp;amp; Tversky, 1973) in probabilistic judgment. The evidential impact perspective derives it from humans' focus on the impact of evidence on belief, rather than conditional probabilities. The Causal Models perspective derives it from humans' inability to integrate information that is causally opaque, as base-rates often are in such experiments. Because causal and evidential-impact relations are often concomitant and confounded, we designed an experiment that specifically teases apart their respective influence on probabilistic judgment. Our results support a combination of the two perspectives, with causal transparency influencing the degree to which one engages in evidential impact reasoning strategies.
Pengaruh Content Creation, Content Sharing, Connecting dan Community Building terhadap Keputusan Pembelian Konsumen pada 3D Stable Equestrian

This research aims to determine the influence of content creation, content sharing, connecting and community building on consumer purchasing decisions on 3D Stable Equestrian. The research method used is a quantitative method. The data used in this research is primary data. The sampling technique uses purposive sampling technique. The sample studied in this research was 100 respondents who were 3D Stable Equestrian consumers. Based on the t test carried out, it can be concluded that partially content creation, content sharing, connecting and community building have a significant effect on consumer purchasing decisions at 3D Stable Equestrian. Content creation, content sharing, connecting and community building simultaneously have a significant influence on consumer purchasing decisions at 3D Stable Equestrian. The coefficient of determination of the content creation, content sharing, connecting and communitu building variables on the purchasing decision variable is 46,3%
Analysis and Development of a Football Scouting App based on Flutter: A Case Study of A3N

Scouting is a crucial aspect of football, where the team's success depends heavily on the ability to select the right player. To minimize risk and optimize resources, teams can utilize the football scouting application. Unfortunately, existing applications still have limitations, especially in support of multiple platforms, and are open source. Therefore, this research is focused on developing a football scouting application that supports multiple platforms and is open source, using a single codebase-based Flutter Framework. Using Flutter, the app can be created based on just one code base, which can be compiled directly on Android and iOS platforms. The developed application has several superior features. First, the application allows the admin to insert player data into the database. Another feature is the ability to search for players based on names that users include. In addition, the application is also able to display a list of players based on the position chosen by the user. Finally, the application can display the complete data and statistics of the currently active player. Through the implementation of the selected development method, the implementation of this system can contribute significantly to supporting the scouting process in football teams. Thus, this application can increase the efficiency and effectiveness of decision-making within the scope of football scouting activities.
Dynamic Knowledge Expansion: Real-time Text Classification with Deep Convolutional Neural Networks

Abstract Convolutional Neural Networks (CNNs) have shown exceptional prowess in text classification in the dynamic landscape of Natural Language Processing (NLP). It is difficult, however, to adapt to evolving concepts when overwhelmed with streaming data and retraining is cumbersome. We present an incremental learning solution tailored for real-world, computationally intensive applications in this paper. Using dynamic updates, we introduce a new taxonomy for text data stream classification that addresses the formidable challenge of concept drift. Using a modest dataset, we train a lean CNN. Using transfer learning, this model intelligently gleans insights from the data stream, leveraging knowledge gained from previous datasets. In order to classify data streams in real time, an online learning mechanism is deployed. A pretrained CNN is updated periodically to capture emerging features, accelerating learning even further. Our experimental results demonstrate the superiority of our approach, setting new benchmarks in performance. Our method allows NLP systems to remain agile, efficient, and state-ofthe- art in an era of relentless data flow.
Development of a mobile learning network for science with augmented reality and its impact on students’ literacy and numeracy

The aim of the current research is to develop the Mobile Learning Network for Science application with Augmented Reality (MLNFS-AR) application on the topic of atomic structure and chemical equilibrium. The study was conducted in four public schools in Jakarta, Indonesia. The Borg and Gall development cycle was adopted in this study, including preliminary research, planning and developing learning media, and validation, evaluation, and revision. Regarding the assessment results from media experts, the mobile learning application was declared very feasible with a percentage of 91.7% for the topic of atomic structure and 87.2% for the topic of chemical equilibrium. The results of the material and language feasibility assessments were 81.6% for the topic of atomic structure and 82.3% for the topic of chemical equilibrium. The results of the media assessments for students were 92.9% for the topic of atomic structure and 89.9% for the topic of chemical equilibrium. The results of the media assessment by chemistry teachers were 97.1% for the topic of atomic structure and 94.7% for the topic of chemical equilibrium. Overall, this mobile learning medium is categorized as very feasible to use as a learning support medium. In addition, the MLNFS-AR app is effective in improving student literacy and numeracy. In sum, the mobile learning app, MLNFS-AR, can be used as a flexible learning medium, can be used anytime and anywhere, and makes it easier for students to learn chemistry.
Zero-knowledge proofs in education: a pathway to disability inclusion and equitable learning opportunities

Abstract In the evolving landscape of global education, the significance of inclusivity and equity has never been more important. Emphasizing the United Nation Sustainable Development Goal 4, this paper explores the innovative application of blockchain-powered Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on disability inclusion. This study introduces a novel disability management system powered by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK). This advanced system enables educational institutions to verify the status of students with disabilities without compromising their personal information, thereby preserving their privacy and reinforcing their identity. This paper evaluates the potential operational efficiency of this prototype system against the existing costs incurred by higher education institutions in disability schemes. It also examines the system’s potential to enhance self-disclosure among students with disability, which is pivotal for their academic success. By advocating for privacy and inclusivity, this study highlights the transformative potential of ZKP in creating an educational environment where students with disabilities can comfortably disclose their needs. This approach not only protects their confidentiality but also empowers them academically, aligning with the global commitment to accessible and inclusive education.
A multi-variable service supply management portfolio model for Telecom industry

Supply operations require some kind of classification according to the features of items. Based on the literature review and expert interview, a multi-variable service supply management portfolio model was developed to classify the procurable service items in the Telecom industry in a more realistic way with better precision. First, a multi-level relational reflective model was built based on four dimensions using the fuzzy Delphi method and the model was validated by factor analysis. Then, 50 service items were placed in 16 clusters of the portfolio model. For each category of services in the model, the pertinent supplier relationship management practices were proposed using the literature review accompanied by a focus group. The presented model contributed to the development of previous theoretical trends in supply management portfolio models by introducing a new dimension, called “pace of innovation in technology”, to those extracted from the literature review, such as ‘strategic importance of the items’, ‘supply risk’, and ‘extent of buyer captivity’. Despite the classical models targeting tangible goods and products, our model was customized for the service industry context and can be considered a starting point towards service supply market segmentation procedures according to the attributes of each service item category.
Enhancing Marketing Transparency and Trust through Blockchain Technology

This research delved into the intersection of blockchain technology and marketing, aiming to investigate its transformative potential in enhancing transparency, trust, and reducing ad fraud. Employing a mixed-methods research design, both quantitative and qualitative approaches were employed to comprehensively address the research objectives. In the quantitative phase, a stratified random sampling technique was utilized to select participants from diverse demographic segments, including both consumers and marketing professionals. Surveys and digital analytics tools were employed to collect data on trust levels, perceptions, and customer engagement in traditional and blockchain-based marketing initiatives. Statistical analyses, including chi-square tests and ANOVA, were conducted to determine the significance of blockchain's impact. The qualitative phase involved purposive sampling of key informants for in-depth interviews, providing nuanced insights into experiences and perceptions regarding blockchain technology in marketing. Content analysis and thematic analysis were applied to marketing materials and qualitative data, respectively. The integration of findings from both phases allowed for a comprehensive understanding of blockchain's impact on marketing, emphasizing its transformative potential. Limitations, such as the challenge of capturing the rapidly evolving nature of blockchain technology, were acknowledged.
The relationship between learning motivation and learning engagement of Chinese state-funded normal university students: The mediating role of professional identity

This study focuses on the relationship between learning motivation, career identity, and learning engagement of state-funded normal university students in Guangdong Province, China. Through intentional sampling, 1449 state-funded normal university students from 10 universities in the Guangdong Province of China were taken as the research objects. Then, reliability and validity analysis, descriptive statistical analysis, difference analysis, correlation analysis, regression analysis, structural equation analysis, the interview method, and other statistical methods are used for analysis. There is a significant positive correlation between learning motivation, professional identity, and learning engagement of state-funded normal university students, and professional identity plays an intermediary role. The results show that the learning motivation of state-funded normal university students has a significant positive effect on professional identity and learning engagement. Furthermore, the professional identity of state-funded normal university students has a significant positive effect on learning engagement; meanwhile, professional identity serves as a partial mediator between learning motivation and learning engagement of state-funded normal university students. Due to the insufficient research level of researchers, there are limited study samples, limited study variables, and limited research methods. This study will improve the quality of education and teaching, stimulate teachers' career development potential, and promote education reform and development. It also fills the research gap, deepens the study theory of learning motivation, and optimize the training mode of state-funded normal university students.
The comparison of students' academic stress in biology class at public and private high schools

&lt;p&gt;Students are demanded by school assignments, and many of them feel under pressure when learning biology. This situation is a trigger for academic stress which has an impact on student achievement. This study aims to identify and compare the factors that influence academic stress on students studying biology at the senior high school level. The research method used was an ex post facto survey using a sample of 54 students with an age range between 16-18 years. Data were obtained through a questionnaire with 9 scales of answer choices and interviews. The results showed that the level of preference for biology subjects for both public and private high school students was quite good, but most students experienced academic stress with a fairly high category in terms of motivation, self-confidence, the education system, teachers, and school conditions. This research implies that teachers are expected to create learning strategies by paying attention to the psychological conditions of students so that student learning outcomes can be maximized.&lt;/p&gt;
Investigating the Effect of Trade Openness and Agriculture on Deforestation in Cameroon

This study aimed to measure the effect of trade openness and agriculture on deforestation in Cameroon from 1980 to 2021 by using a fully modified ordinary least squares (FMOLS) approach. Data used are from the World Bank and FAO. The results obtained indicate that when trade openness increases, deforestation also increases, but when trade openness increases up to a certain threshold, deforestation decreases. This study also reveals that agriculture is one of the major causes of deforestation in Cameroon. Agricultural output and agricultural value-added both have a positive and significant impact on deforestation. There is an inverted curve relationship between economic growth and deforestation in Cameroon, this shows that the EKC is respected with deforestation as it is postulated that at higher levels of income, GDP turns to reduce deforestation meaning a unit change in GDP2 leads to a reduction of deforestation. We recommend the implementation of concrete actions and strict environmental policies focused on a green economy, to control the exploitation of natural resources with particular attention to the sustainable exploitation of wood. Sustainable agricultural practices should also be implemented, as well as more suitable liberal trade policies.
Design Education Methodology Using AI

In this comprehensive research paper, the authors scrutinize the integration of Generative Artificial Intelligence (GAI) into design education methodologies with a focus on the twin objectives of enhancing creativity and operational efficiency. Leveraging an array of research methods ranging from literature reviews to case studies and empirical analyses the paper presents a well rounded exploration of how emerging AI technologies can redefine design education. Notably, the study highlights the immense potential of AI-powered tools in augmenting creativity. It asserts that GAI can act as a catalyst for spawning novel creative ideas, diversifying design variations, and expediting the exploration of complex design alternatives all within a constrained time frame. The methodology involves an objective and statistical analysis of work generated during a workshop, offering insights into the relationship between human creativity and AI-enabled design processes. Despite the palpable benefits, the study also uncovers a set of challenges hindering seamless AI integration into design curricula. These challenges range from the need for specialized, in-depth training on AI technologies for designers to mastering the nuances of prompt engineering. The paper also raises ethical considerations related to AI-generated designs, as well as flags potential biases present in AI algorithms. The authors conclude by stressing the imperative of acknowledging and addressing these challenges head-on to ensure both responsible and effective incorporation of GAI into design education.
Clustering Fake News with K-Means and Agglomerative Clustering Based on Word2Vec

Fake News on digital platforms is a major problem in this digital age. Many people want to find methods to detect Fake News. This research looks at a way to group Fake News articles using K-Means and Agglomerative Clustering techniques, using the semantic representations from Word2Vec embeddings. The researchers use natural language translation methods and advanced machine learning to improve the accuracy and efficiency of Fake News detection. The study involves getting meaningful features from textual data, turning them into vector representations using Word2Vec, and then applying clustering algorithms to sort similar articles. The methodology aims to improve the most recent state of the art in Fake News detection, helping to create more reliable and robust tools to fight misinformation in the digital age, In the comparative analysis of clustering metrics, K-Means clustering exhibits a Purity Score of 88.09% and an Adjusted Rand Score of 58.03%. Conversely, Agglomerative Clustering with the Ward method yields a Purity Score of 85.13% and an Adjusted Rand Score of 49.36%.The Purity Score of 88.09% for K-Means suggests a strong ability to form clusters where the majority of data points share the same true class. Agglomerative Clustering with Ward, though slightly lower at 85.13%, also demonstrates effective class separation within clusters. When considering the Adjusted Rand Score, which accounts for chance and measures the agreement between true and predicted labels, K-Means significantly outperforms Agglomerative Clustering with Ward. The scores are 58.03% and 49.36%, respectively