Reviewerly/exHarmony huggingface dataset:
Stylistic Analysis of Body Positivity Movement Campaign on Pinterest

The emergence of body positivity movement has been popular in online media as the result of society’s awareness towards the phenomenon of body image shaping by media, culture, as well as society itself. The concept of ideal body image has forced people especially women to meet the standard that is often demanding and endangering. Moreover, the rise of social media worsens this phenomenon as social media users are now more exposed to those images. Many parties that have concern in this issue show their concern in bringing awareness to people by posting banners containing body positivity campaign in online media platform, one of which is Pinterest. This research is aimed at investigating the use of figurative language in body positivity movement campaign from the perspective of stylistic analysis. The lexical and syntactic structures of the utterance are also identified. The result shows that from 33 data collected, the type of figurative language used are personification, metaphor, paradox, hyperbole, simile, and synecdoche in which personification is found to be the most frequent figurative language in the banners. The use of phrase and simple sentence are also the most dominant lexical and syntactic structures used by the creator.
THE INFLUENCE OF WORD OF MOUTH (WOM) AND PROMOTION ON INTEREST IN WATCHING INDONESIAN FILM AT CINEMA IN KARAWANG CITY

The research was conducted using descriptive and verification methods, namely collecting, presenting, analyzing, and testing hypotheses, as well as making conclusions and suggestions. A sample of 221 respondents from a population of 88062 viewers and processed using SPSS25. The results of this study are in the agreed criteria and are good in all variables. The correlation coefficient between the independent and independent variables has a strong and direct correlation level because the value is positive and has a significant correlation. Word of Mouth contributes less to Interests than Promotions. The results of the analysis of the simultaneous influence of Word of Mouth and Promotion on Interest 37.9%, with sig criteria then H0 is rejected because it is said that Word of Mouth and Promotion have a significant influence on Interest. While the remaining 66.2% is the contribution of other variables (ɛ) which were not examined in this study.
Collaboration of a STEM-Based Blended Learning Model to Enhance Mathematical Comprehension

Paradigms in the era of digitalization, particularly in the field of education in the use of Blended Learning in collaboration with the STEM approach (Science, Technology, Engineering, and Mathematics) as a guide for teachers in technological development capable of raising the standard of education in Indonesia. In class 5 SD N 3 Bangsa, a quantitative sampling model with probability is used as the research methodology. using observational and questionnaire data gathering methods, up to 32 students served as respondents. According to the statistical analysis, there is a substantial correlation between STEM-based Blended Learning and primary school student grasp of mathematics, with a significance value of 0.668. Blended Learning with a STEM focus significantly enhances students' mathematical comprehension of cube nets. Students struggle with smartphone operation uncertainty. It is okay to apply this learning technique for teaching social sciences, math, and science.
Research Classification using the Malaysian Research and Development Classification System (MRDCS)

Classifying research projects or research publication into research areas is crucial in many statistical analyses including for example in bibliometric analysis. Research publication classification usually takes place at the level of journals, where subject categories based on available databases like the Web of Science or SciVal are some of the popular classification systems. However, journal-level classification systems have limitation especially when classifying multidisciplinary journals. To overcome this limitation, it is suggested that a classification system that can classify research be constructed based on the areas of research proposals and their related publications. Successful grant applications/research projects and their publications were clustered into research areas based on the Malaysian Research and Development Classification System (MRDCS) (6th Edition). A total of 1738 research projects managed by Universiti Malaya’s Research Cluster from year 2015 to 2017 were mapped. The strengths and the limitations of the proposed classification system are also discussed. Results indicate that out of the 20 research categories, Medical and Health Sciences, Social Sciences, Economics, Business and Management, Humanities, and Engineering and Technology emerge as the top five research categories with the highest critical mass value based on the number of projects. Classification mapping also found Engineering and Technology, Medical and Health Sciences, Material Sciences, Social Sciences, and Applied Sciences and Technologies to be the top five research categories that produced the highest number of outputs in terms of publications.
Performance Improvement of K-Nearest Neighbor Algorithm in KIP Scholarship Recipient Selection

Abstract&#x0D; Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
High-Level Grammars for Visualization and Visual Analytics

The increase in data availability and the popularization of data science brought a continuously growing need to use visualizations to explore complex data. Creating new visualizations, however, is a difficult task that often requires extensive programming expertise. High-level grammars for authoring visualizations provide a systematic and flexible way to specify visualizations in a declarative manner, lowering the entry barrier for data analysis. Such grammars effectively assist in the exploration of the visualization design space and of complex datasets. The goal of this tutorial paper is to then provide a summary of recently proposed grammars, so that a broad audience (e.g., students, researchers, practitioners) can better understand how they are designed and used in practice for visualization and visual analytics tasks.
Associating Anomaly Detection Strategy Based on Kittler’s Taxonomy with Image Editing to Extend the Mapping of Polluted Water Bodies

Anomaly detection based on Kittler’s Taxonomy (ADS-KT) has emerged as a powerful strategy for identifying and categorizing patterns that exhibit unexpected behaviors, being useful for monitoring environmental disasters and mapping their consequences in satellite images. However, the presence of clouds in images limits the analysis process. This article investigates the impact of associating ADS-KT with image editing, mainly to help machines learn how to extend the mapping of polluted water bodies to areas occluded by clouds. Our methodology starts by applying ADS-KT to two images from the same geographic region, where one image has meaningfully more overlay contamination by cloud cover than the other. Ultimately, the methodology applies an image editing technique to reconstruct areas occluded by clouds in one image based on non-occluded areas from the other image. The results of 99.62% accuracy, 74.53% precision, 94.05% recall, and 83.16% F-measure indicate that this study stands out among the best of the state-of-the-art approaches. Therefore, we conclude that the association of ADS-KT with image editing showed promising results in extending the mapping of polluted water bodies by a machine to occluded areas. Future work should compare our methodology to ADS-KT associated with other cloud removal methods.
Rethinking literacy pedagogy in the context of 4IR: Learners’ access and reading motivation

Background: In the education sector, technologies are (or can be) repurposed in the Fourth Industrial Revolution (4IR) to facilitate teaching and learning. However, this shift may impact on learners’ access to reading materials and reading motivation.Objectives: The aim of the study was to determine whether Grade 8 English First Additional Language (FAL) learners’ motivation to read is influenced by access to reading materials in a traditional classroom setting or through online reading platforms.Method: Likert-scale survey questionnaires were used to measure this relationship. Data from 466 Grade 8 English FAL learners from six South African township schools were collected and SPSS version 25 was used to analyse the data.Results: In traditional classrooms, the findings showed that increased access positively influenced motivation, whereas this effect was not evident in online reading platforms. However, in both settings, motivation increased when learners had access to a diverse selection of reading materials. Finally, the findings indicated a parallel relationship between learners’ low motivation and limited access to reading platforms.Conclusion: The study concluded that while traditional classrooms continue to play an important role in global education, the ongoing evolution of technology-based learning platforms has compelled a shift in reading practices toward online platforms, which are reshaping the educational landscape, demonstrating the increasing significance of technology-mediated learning in modern education.Contribution: The authors recommend enhanced collaboration between teachers and learners to devise innovative methods for motivating online reading, ensuring learners’ inclusion in the 4IR literacy pedagogy evolution.
Visual Analytics Using Heterogeneous Urban Data

Several cities contain informal settlements in high-risk areas, particularly mountainous regions. With growing concerns around climate change, these areas are more susceptible to environmental disasters, such as floods and landslides, where topography plays a key role. Fortunately, the availability of data describing complex 2D and 3D aspects of urban regions opens new opportunities to tackle these challenges. This paper aims to integrate these data sets to support human-centered solutions. Based on a collaboration with urban planning professionals, we first contribute a data integration framework that takes data from multiple sources with different mathematical descriptions, dimensions, and resolutions and creates a data-enriched triangle mesh that can be used not only for visualization purposes but also in geometry processing and numerical simulation applications. Second, we contribute a visual analytics system that uses the enriched triangulation to enable stakeholders and domain experts to jointly explore a complex and heterogeneous collection of data sets to identify landfall and flood risk areas. Finally, the usefulness of our approach is highlighted through a case study in collaboration with an urban planner interested in risk areas.
A hybrid intelligence approach for circulating tumor cell enumeration in digital pathology by using CNN and weak annotations

Counting the number of Circulating Tumor Cells (CTCs) for cancer screenings is currently done by cytopathologists with a heavy time and energy cost. AI, especially deep learning, has shown great potential in medical imaging domains. The aim of this paper is to develop a novel hybrid intelligence approach to automatically enumerate CTCs by combining cytopathologist expertise with the efficiency of deep learning convolutional neural networks (CNNs). This hybrid intelligence approach includes three major components: CNN based CTC detection/localization using weak annotations, CNN based CTC segmentation, and a classifier to ultimately determine CTCs. A support vector machine (SVM) was investigated for classification efficiency. The B-scale transform was also introduced to find the maximum sphericality of a given region. The SVM classifier was implemented to use a three-element vector as its input, including the B-scale (size), texture, and area values from the detection and segmentation results. We collected 466 fluoroscopic images for CTC detection/localization, 473 images for CTC segmentation and another 198 images with 323 CTCs as an independent data set for CTC enumeration. Precision and recall for CTC detection are 0.98 and 0.92, which is comparable with the state-of-the-art results that needed much larger and stricter training data sets. The counting error on an independent testing set was 2-3% and 9% (with/without B-scale) and performs much better than previous thresholding approaches with 30% of counting error rates. Recent publications prove facilitation of other types of research in object localization and segmentation are necessary.
Application test of PCA based improved K-means clustering algorithm in analyzing NGO assistance needs in less developed countries

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Design &amp; Development of A 3D Scanner based on a Distance Sensor to Scan Real-life Objects &amp; Implementing in Digital Reality

This research aims to scan a real-world 3D object, reconstruct its digital model with internal hollow space data (volume, area), and blend it into augmented reality. When it comes to rebuilding a body, precision is a significant necessity. To scan a three-dimensional object, numerous methods are available. Most of which can only determine the outside dimensions of the entity being scanned. They barely provide any information regarding the hollow surface of the body. And to ensure accuracy, expenses may spike. To reconstruct a 3D object, the distance (e.g., Lidar, GP2Y0A41SK0F, Ultrasonic, etc.) sensor-based 3d scanner comes into play. The proposed system also includes a feature that can measure the hollow space inside the object. The distance sensor bounces laser light, infrared rays, or ultrasound to gauge distance and depth to form a 3D scan. The sensor sends that data to the microcontroller, and then the microcontroller brings that cartesian coordinate data to computer software to build the mesh. For detecting internal hollow space, gamma resonance is used. Furthermore, after scanning an object, Stereolithography or Film box files may be used for various purposes, including integrating with augmented reality, generating 3D prints, creating a video game or CGI avatars, etc. This project can create 3D scanned mesh quickly and accurately of real-world items. The 3D scan data regenerated by the proposed system can merge with augmented reality to simulate the metaverse. Scanning a three-dimensional body can be useful in our everyday lives as well as for research and experimental purposes. This proposed system provides an unbiassed novel strategy for reconstructing 3D internal hollow space with significant accuracy. This research addresses the hypothetical subject of what impact real-world objects portrayed in the digital world will have on mankind's new cyber world.
Implementation of Hydrocarbon Adventures Game as An Online Learning Medua During Covid-19 Pandemic

The purpose of this study was to determine the application of the game Hydrocarbon Adventures as an online learning medium for hydrocarbon compounds in the covid-19 pandemic. The design of this study used the one-group-pretest-posttest experimental method. The target of this research is online learning using Hydrocarbon Adventures media games conducted by 27 grade XI students of Senior High School 1 Sampang. The students will be given a pretest before using the game and post test after using it. After that, students will be given questionnaire for the learning using online learning media. The results showed that student learning outcomes after using the learning media Hydrocarbon Adventures game had increased with classical completeness for values reaching 92.59%, and an increase in student learning outcomes at an average pretest value of 44.19 into an average post test score amounting to 83.41. Student responses about game media were obtained 79.2%. Based on these results, it was concluded that the Hydrocarbon Adventures learning media on the material of hydrocarbon compounds through online learning during the covid-19 pandemic can improve student learning outcomes.The purpose of this study was to determine the application of the game Hydrocarbon Adventures as an online learning medium for hydrocarbon compounds in the covid-19 pandemic. The design of this study used the one-group-pretest-posttest experimental method. The target of this research is online learning using Hydrocarbon Adventures media games conducted by 27 grade XI students of Senior High School1 Sampang. The students will be given a pretest before using the game and post test after using it. After that, students will be given questionnaire for the learning using online learning media. The results showed that student learning outcomes after using the learning media Hydrocarbon Adventures game had increased with classical completeness for values reaching 92.59%, and an increase in student learning outcomes at an average pretest value of 44.19 into an average post test score amounting to 83.41. Student responses about game media were obtained 79.2%. Based on these results, it was concluded that the Hydrocarbon Adventures learning media on the material of hydrocarbon compounds through online learning during the covid-19 pandemic can improve student learning outcomes.
Some recent advances in random walks and random environments

Recent contributions to random walks in random environments and related topics are presented. We focus on non parametric estimation for one dimensional random walks in random environment and on the Dirichlet distribution on decomposable graphs.
Nuclei Detection in HER2-SISH Histopathology Images

Automatic quantification of cell nuclei in silver-enhanced in situ hybridization (SISH) images can be of great help to pathologists to examine HER2 status based on HER2 and CEN17 biomarkers. This paper proposed an image processing-based method for nuclei detection in HER2-SISH images. We first extracted sections of the foreground image using a combination of local thresholding, morphological filtering, and expanding regions based on intensity. Then the marker-controlled watershed is applied for separating the clustered nuclei in the foreground regions. A set of nuclei marked by our collaborating pathologists on SISH-stained breast cancer images are used to measure the effectiveness of the proposed approach. HER2-SISH histo-scoring is highly dependent on the accurately identified nuclei, hence the importance of the proposed detection method. Experimental results shows very promising detection performance, with high concordance against the pathologists’ marking.
Hybrid approach for Word Sense Disambiguation in Marathi Language

Word Sense Disambiguation (WSD) is the capacity to compute the appropriate meaning of a word in a particular context. Among the top languages, one of the languages is Marathi, which is also the mother tongue of state Maharashtra, India. Marathi language has many words, phrases which have the multiple uses and can be used at various places in the sentences interpreting different meaning. This ambiguity is well identified by humans manually, but for machine or artificial intelligence, it is not that easy and work is required to develop the models which can identify the accurate meaning interpretations for such words used in a sentences. Therefore, it is inspiring to create a corpus of Marathi that would communicate the accurate meaning of a word with ambiguity. Some of the existing WSD systems use various supervised learning algorithms which classifies the meaning of the word in the given sentence. The approaches that are available are limited to the small size datasets and also have the accuracy issues. Here, the efforts are taken to improve the accuracy of such WSD system for the Marathi language using combination of knowledge based approach and machine learning technique such as Support Vector Machine (SVM). The model is trained with the real time dataset prepared by taking the help of Marathi language experts and real time environment is created for better results and working of the system.
Optimizing the development and utilization of libraries through collaboration

Background of the study: Collaboration in the library sector is essential to fulfill the library users' information needs. Purpose: The objective of this study is to investigate the methods, types of collaboration, and challenges involved in the implementation of cooperative efforts between the West Java Dispusipda and UNIKOM Library, to enhance the development and utilization of the library. Method: In this research, a qualitative approach is employed to analyze the data, which is subsequently processed through the stages of data reduction, data presentation, and drawing conclusions. Findings: The collaboration between the West Java Dispusipda and UNIKOM Library is based on a policy that outlines the roles, responsibilities, and scope of the cooperation. The agreed activity plan includes various initiatives such as enhancing the quality of human resources, exchanging experts, conducting research cooperation, implementing science and technology applications, and collaborating in other areas. However, due to the Covid-19 pandemic, not all planned activities have been fully implemented. One notable achievement is the accreditation of the UNIKOM Library. Conclusion: Cooperation is an effort to achieve library goals
Hyperchaotic technology-based efficient image encryption algorithm an overview.

Multimedia data encryption is so crucial because the multimedia encryption algorithm needs more time and memory, and it is difficult to implement. Because of this, the hyperchaotic image encryption technique is becoming more and more popular, which uses little memory, time, or energy and offers the highest level of security for low-powered devices. This study offers a comprehensive overview of modern hyperchaotic systems. By focusing on these complex systems' uniqueness and fundamental features, a study of their dynamic behavior is offered. Such systems are now being used more and more in a variety of industries, including finance, secure communication, and encryption, for example. In reality, every field calls for particular performances of unusual complexity. This research then suggests a specific classification based on the crucial hyperchaotic characteristic, Lyapunov exponent, the equilibrium points, dynamical behavior, NPCR, and UACI..