Reviewerly/exHarmony huggingface dataset:
Examining/Understanding the Reading Culture and Comprehension Abilities of Learners of English as a Foreign Language in Uganda

In this study, I analysed the reading culture and its influence on reading comprehension of learners of English as a Foreign Language (EFL) in Uganda. The data were collected by interviewing ten EFL teachers in order to gather their views of their learners’ reading culture and comprehension abilities. Data were analysed using Vroom’s Expectancy theory. Findings indicate that the EFL learners were enthusiastic about reading even beyond the classroom reading tasks. 30% of the teachers interviewed confirmed that the learners had a positive attitude towards reading as part of their language learning, hence a confirmation of their good reading culture. The rest of the teachers interviewed confirmed that 70% of the learners possess a poor reading culture. The study further confirmed the presence of a strong link between the learners’ reading culture and reading comprehension achievement. The majority of the teachers agreed that the positive reading culture exhibited by their EFL learners significantly contributed to their reading comprehension. The learners with a poor reading culture also exhibited poor reading comprehension abilities because they lacked frequent reading comprehension opportunities. The study concludes that the majority of EFL learners lack a good reading culture. There was a clear relationship between the reading culture and the learners’ attainment of reading comprehension. This is confirmed by the fact that in cases where the reading culture was positive, the learners’ reading comprehension abilities were better
1313 UltiStacker™: easy to use multimodal image co-registration with sub-cellular accuracy

<h3>Background</h3> Ultivue InSituPlex technology (ISP) enables whole-slide multiplex immune-fluorescence (mIF) staining of multiple protein targets on an autostainer and for conducting multiple rounds of ISP imaging and a terminal or initial H&amp;E stain of the same slide. But to accurately phenotype each cell, one must co-register or ‘stack’ the images of each round with sub-cellular accuracy. This can be a demanding task due to scanner artifacts (<i>e.g.,</i> field selection, stitching deformations, focus errors), staining variations and possible tissue damage during the staining process. Here we describe Ultivue’s UltiStacker™, an easy-to-use Windows OS application that performs whole slide image co-registration from a variety of scanners with high accuracy and high throughput. <h3>Methods</h3> The DAPI channel of each ISP round is used to perform the co-registration. For H&amp;E images, a ‘synthetic DAPI’ image is produced by defining the color of the nuclear stain in Hue Saturation Value color space. H&amp;E images may also need to be corrected for pixel size differences and rotation with respect to the IF images. The base (1<sup>st</sup> round) image is divided into a regular grid of overlapping tiles and the offset of each base tile to its location in each round image is found using Normalized Cross-Correlation. The offsets are then regularized and used to restitch each round image to match the base image. The accuracy and precision of the co-registration of the final stack is then independently validated. <h3>Results</h3> We show evidence that the algorithm can routinely co-register IF rounds with an accuracy of 1µm (~3 pixels at 20X) over the whole slide. Similar results are also seen for H&amp;E matching for good quality samples with high nuclear density. The algorithm is also tolerant to regions of scanning or staining artifacts, tissue damage or loss, etc. With built-in parallel processing, most samples can be co-registered in a few minutes to an hour, depending on tissue area, staining quality, scanner type, hardware performance, etc. <h3>Conclusions</h3> UltiStacker provides a convenient means to co-register and therefore phenotype all the cells of whole slide images across multiple rounds of fluorescent and brightfield scanning. It reads several common image formats and can combine IF, RNA-ISH UltiStacker can routinely obtain 1µm co-registration needed to accurately phenotype cells across multiple rounds of mIF imaging and it enables visualization and analysis of those cells in multiple modalities for a greater understanding of the Tumor Microenvironment.
Effects of the Firefly Optimization Algorithm Hyperparameters on the Optimal Placement Problem Results of Renewables-Based Power Plants

Integration of new capacities of renewable energy sources into existing electrical energy systems is one of the most relevant problems in the development of the power industry for most countries. The geographical location for construction should be accurately selected to achieve the high performance and efficiency of that generation type. The problem of proper placement of renewable energy sources generation could be solved with expert knowledge or with the help of mathematical optimization algorithms. This article is devoted to the consideration of renewable energy sources' optimal placement problem-solving with the use of the metaheuristic optimization algorithm named as the firefly algorithm. Values of the capacity factor for the renewable energy power station were used as the objective function to evaluate the proposed algorithm. Capacity factor values were calculated using a machine learning regression model based on the random forest algorithm. The proposed algorithm was tested in the case of placement optimization of a photovoltaic power plant with an installed capacity of 15 MW in Belarus. The article presents the influence of hyperparameters on the optimization results of the algorithm. Results are shown in the article in the form of boxplot diagrams of optimal capacity factor values, which were found while five hyperparameters were changed separately.
A k-Nearest Neighbor Approach for Domain-Specific Translation Quality Estimation

Translation quality estimation (QE) is used to assess the quality of machine translation (MT) output without using reference translations. Although QE technology in general domain has made significant progress, it still faces challenges in specific domains due to limited data availability and expensive annotation costs. To address this issue, this paper proposes a kNN-QE method, which provides instance-based augmentation for the model by querying the built datastore during prediction. This method does not require an explicit training process and improves the prediction accuracy of the model without spending additional time and computing resources to train the model. This paper further improves the model performance by adjusting the loss function to alleviate the problem of QE data label bias. Experiments on two domain-specific datasets show that the proposed method achieves significant improvements over the baseline method on word-level QE tasks.
Analisis Peran Guru Dalam Upaya Meningkatkan Kemampuan Membaca Permulaan Siswa Kelas III Di SDN 08 Bungkang

The background of this research is that there are still grade III students who have not been able to read well, students' reading abilities are still in recognizing letters, and a lack of attention to students who experience these problems. beginning reading for class III students at SDN 08 Bungkang This research method is a qualitative method with a qualitative descriptive approach. Collecting data using interview techniques, questionnaires and documentation. Subjects and research objects of class III teachers and class III students with a total of 10 students consisting of 4 male students and 6 female students. The data analysis technique uses 3 stages, namely, data reduction, data presentation and conclusion. The research results by analyzing 6 indicators of the teacher's role, namely, the teacher as educator, teacher, mentor. directors, trainers and assessors showed that of the 6 indicators of the teacher's role it was still lacking in its application, because there were several inhibiting factors for teachers and students in the learning process. The barrier factor is the lack of learning media (books) for the ease of student learning. The conclusion of this study is that the level of application of the teacher's role in an effort to improve the reading ability of the beginning graders of class III SDN 08 Bungkang is classified as "Good".
Performance Analysis of Distributed Learning in Edge Computing on Handwritten Digits Dataset

Deep learning models often consist of millions or even billions of parameters, making it challenging to deploy them on devices with limited resources. Therefore, this study presents scenarios to assess the computational capability of edge devices to provide an evaluation of the learning performance of distributed learning methods. It focuses on using Deep Neural Network and the handwritten digit dataset (MNIST) in edge computing to evaluate the performance of distributed learning methods (no-offloading, full-offloading, split computing, and federated computing) in both ideal and realistic conditions. The performance evaluations are based on Precision, Recall, Accuracy, F1-score, and Estimated time complexity. The findings indicate that the full-offloading method achieved the highest performance in ideal conditions. However, in realistic situations, the split computing and federated computing methods performed better than the others.
A LLM-Based Simulation Scenario Aided Generation Method

In the simulation training system, the generation of simulation scenarios is a basic problem that needs to be studied. Firstly, expounds on the technical characteristics of LLM and knowledge graph; then structurally describe the simulation scenario related content, and build scenario knowledge graph; according to the characteristics of scenario aided generation, a simulation scenario generation method based on LLM is proposed, which uses prompt to fuse knowledge graph and LLM, next, the implementation steps of this method were elaborated; finally, the specific application proves that the method proposed in this paper is a good reference for the generation of simulation scenarios.
An Intelligent Article Knowledge Graph Formation Framework Using BM25 Probabilistic Retrieval Model

Knowledge graphs are considered as the best practice to illustrate semantic relationships among the collection of documents. The research article presents an intelligent article knowledge graph formation framework that utilizes the BM25 probabilistic retrieval model for constructing a knowledge graph to express conditional dependency structure between articles. The framework generates conditional probability features by employing the Okapi BM25 Score and rank documents according to the search query. Also, the CRF model is trained to perform Named Entity Recognition to empower semantic relationships. This indicates that a BM25 score feature matrix is created of all named entity in the collection which comes out as high dimensional. This problem is rectified using singular value decomposition dimension reduction technique and pairwise cosine similarity is calculated on reduced dimensions. Based on a similarity value, the documents are connected and construct the knowledge graph structure. The results represent the outcome of the search engine for different types of queries on IMDB database and constructed knowledge graphs based on similarity between articles in BBC and IMDB Dataset.
The Role of Central Bank Digital Currencies (CBDCs) in Reshaping Monetary Policy: A Global Analysis of Economic Implications and Implementation Challenges

Purpose:The research seeks to understand the significance of Central Bank Digital Currencies (CBDCs) in influencing and reshaping monetary policy on a global scale. It delves into the potential economic consequences, both positive and negative, of CBDC implementation, and the challenges central banks might face during the implementation phase. Theoretical framework: The paper leverages on the modern monetary theory, digital economy frameworks, and the evolution of digital finance. It combines these constructs with the historical role of central banks to hypothesize how CBDCs could redefine traditional monetary structures. Findings:Results show that CBDCs have the potential to significantly enhance monetary policy effectiveness, especially in realms of financial inclusion, transmission mechanisms, and real-time data acquisition. However, challenges such as technological limitations, privacy concerns, and international coordination issues could impede their widespread adoption. Research, Practical &amp; Social implications: The findings offer central banks a comprehensive guide to anticipate potential challenges and benefits of CBDCs. From a societal perspective, the research highlights how CBDCs can transform financial accessibility and literacy, but also underscores the importance of addressing privacy and security concerns. Originality/value:While numerous studies have evaluated the conceptual design of CBDCs, this research stands out by providing a holistic global analysis of their actual economic implications and practical implementation challenges. It serves as a reference for policymakers and stakeholders in the evolving monetary landscape.
Power image retrieval model based on serial code verification

Based on the principle of coding check, this paper proposes a deep hash method of serial hash coding, serial hash coding network, which is different from traditional hash coding method. In this method, the hash coding network layer structure is designed as a serial mode, and the previous part of the hash code generated by the serial is checked in the process of generating the hash code, so as to make full use of the relevance and redundancy of the encoding to generate the hash code with more information, more compact and stronger discrimination. mAP is used as the evaluation criterion for retrieval performance, and the proposed method is compared with the current mainstream hash method. The experimental results show that the method has good effectiveness.
Islamic Perspective in E-Service Quality-based Knowledge Sharing on Islamic Hospital Employee Performance Improvement

Technological developments now greatly affect the activities of health organizations and are expected to encourage better hospital performance. One of the efforts that can be made to increase employee knowledge related to technological developments is through knowledge sharing. Measuring the quality of hospital management information systems from the user’s point of view using the e-service quality method. The study aims to determine the effect of knowledge sharing and e-service quality information systems on employee performance in Islamic hospitals that have followed global budget rules in West Java. This research uses a quantitative method with a causal research type. Information is collected directly from the field to find out the opinion of the respondents on the object being studied. The researchers used judgment sampling with the number of samples obtained using the Bernoulli method with an error of 5% so that the minimum number of respondents was 128. The research data were obtained through the results of interviews and questionnaires. The data were processed, analyzed, and then conclusions were drawn using the cross-sectional method. The results of the analysis show that both knowledge sharing and e-service quality are in good category and employee performance is in the very good category. The variables of knowledge sharing and e-service quality both partially and simultaneously have a positive and significant effect on employee performance with a moderate effect.&#x0D; Keywords: e-service quality, employee performance, Islamic hospital, knowledge sharing
Centering the Human: Digital Humanism and the Practice of Using Generative AI in the Authoring of Interactive Digital Narratives

This paper explores the application of chat-based Generative AI (GenAI) in the Interactive Digital Narrative (IDN) authoring process, advocating for a human-centered approach rooted in a Digital Humanist perspective. It scrutinizes GenAI’s capacity to augment human narrative creation and unveils the complexities inherent in its integration into IDN authoring. The potential risks tied to GenAI’s incorporation, including data exploitation, displacement of human labor, and the potential diminishment of human agency and creativity, are thoroughly examined. Offering a precautionary viewpoint, this work outlines Digital Humanist principles to guide GenAI’s use in authoring, which includes elevating organic creativity and human agency. Further, emphasizing the need for transparency and accountability, the author underscores the importance of maintaining a harmonious, human-led creation process to serve the social good. The aim is to center the human elements of authoring while ethically leveraging GenAI’s capabilities, paving the way for a future where IDNs embody collective human values and uphold creative integrity.
QNNRepair: Quantized Neural Network Repair

We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quantization. It accepts the full-precision and weight-quantized neural networks, together with a repair dataset of passing and failing tests. At first, QNNRepair applies a software fault localization method to identify the neurons that cause performance degradation during neural network quantization. Then, it formulates the repair problem into a MILP, solving neuron weight parameters, which corrects the QNN’s performance on failing tests while not compromising its performance on passing tests. We evaluate QNNRepair with widely used neural network architectures such as MobileNetV2, ResNet, and VGGNet on popular datasets, including high-resolution images. We also compare QNNRepair with the state-of-the-art data-free quantization method SQuant [22]. According to the experiment results, we conclude that QNNRepair is effective in improving the quantized model’s performance in most cases. Its repaired models have 24% higher accuracy than SQuant’s in the independent validation set, especially for the ImageNet dataset.
Mother and Children Literacy Model at Home

The literacy skill of Indonesian children has been ranked below average in the world. In fact, literacy skills are needed by every individual. Therefore, immediate action is needed starting from the closest environment, namely by the family to foster a literate soul in children. In addition, the mother plays a vital role in her child’s life, as she is the one most closest. Therefore, in order to determine a suitable and interesting literacy model for mothers and children, it is necessary to know how to carry out mother and children literacy activities at home. This research aims to examine the implementation of mother and children literacy activities carried out by mothers having elementary and junior high school education backgrounds in Lamajang Village, Bandung Regency. This study uses a descriptive method by collecting data using a questionnaire administered to 20 respondents. The findings show that the most used media in literacy activities at home is story books that have moral and religious messages. However, there has not been any specific method aligning to the children’s development used by the mother. It was found that they chose activities that combined children’s speaking and listening skills. In addition, children’s interest occurred when the stories delivered by the mother involved the children in the creation of a meaningful work.&#x0D; Keywords: learning model, mother and children literacy, home
Differential Cryptanalysis with SAT, SMT, MILP, and CP: A Detailed Comparison for Bit-Oriented Primitives

SAT, SMT, MILP, and CP, have become prominent in the differential cryptanalysis of cryptographic primitives. In this paper, we review the techniques for constructing differential characteristic search models in these four formalisms. Additionally, we perform a systematic comparison encompassing over 20 cryptographic primitives and 16 solvers, on both easy and hard instances of optimisation, enumeration and differential probability estimation problems.
For whom is data literacy empowering? An awareness-action typology

Building on recent empowerment perspectives on data literacy, we examine how students and working adults talk about their understanding of data and report on their own personal-data-related practices. Through a deductive and inductive analysis of interviews with 19 subjects ranging from middle school to middle age, we find that awareness and action with respect to data consumption and production do not necessarily increase in tandem. For example, being more aware of the data that can be used to track them does not make individuals more likely to take action to manage their personal data. While some feel anxiety about the gap between knowledge and action, others resolve the tension by choosing not to care. These findings are synthesized in a typology of personas in the space of data awareness and action. We investigate the relationship between age and educational attainment with location in this awareness-action space and discuss implications for data literacy education.
A Study on the Promotion and Health Concept of Short Videos on Exercise and Fitness among College Students on Douyin App

The development and popularity of short video applications, represented by Douyin, have collided with the public health issues that have received unprecedented attention in the post-epidemic era after the outbreak of COVID-19, providing a favorable environment for the development of short videos on sports and fitness. This study uses quantitative and qualitative research methods to investigate the users behaviours, college students sense of experience watching short videos on exercise and fitness and the influence of their health concepts. Also, we analyze specific cases to summarize the content creation patterns of such videos targeting college students and the transmission patterns of health concepts in these videos. The study aims to provide a theoretical reference for better serving university students with these videos on the Douyin platform. The study found that the videos on the Douyin platform are viewed by university students with distinctive group characteristics and that the content creation of these videos targeting university students is tailored to the actual needs of university students, forming an interactive mechanism between the bloggers who publish videos and the audience, using the role of companionship and role models as a medium for the transmission of health concepts in these videos.