Reviewerly/exHarmony huggingface dataset:
When I Cry Into My Icosahedral Mermaid Womb, The Sea Cradles Me: Creating Imaginary Water Worlds For Suicidal Depression and Healing Through Multisensory Environments

Imagine a future where technology is liquid, where the human senses are entirely wet, foggy, mushy, and gooey. Where everything and everywhere is liquefied and watery. Where humans evolve into pluviophile (love of rain) and hydrophile species. Imagine what state our depressed and suicidal bodies would react to and sustain in this hyper-watery world. This thesis set out to reimagine and recreate my personal experiences with depression, anxiety, and suicidal behaviours through a wet, watery, emotional, and hyper-futuristic narrative. The project aimed to design and create a series of immersive multi-sensory environments that provide therapeutic and healing spaces for mental health and well-being. Thus I have been informed by my personal story through imaginary water worlds and speculative fiction writing as a method of inquiry. These immersive multi-sensory works explore various emerging creative technologies to help realise my thesis project, such as using sound, interactive installation, digital, and extended reality (XR) technologies. I have used my own body, bodily experience, and personal feelings as a storytelling tool and primary material for my master's project. Water plays a significant role in my creative research and practice. Also, it hugely influences my writing, providing therapeutic and healing qualities. The approach to my research has drawn from methodological perspectives, including autophenomenography, first-person narrative, sensory ethnography, and speculative fiction through creative practice. I am using 'water' as a metaphorical guideline for methodological approaches to tie together as one, connecting these bodies of watery perspectives into an oceanic methodology. The significance of the research is the engagement and immersion of the multi-sensory environments through creating imaginary water worlds that offer a space for others to swim, dive, and acknowledge these critical issues relating to mental health by sharing personal stories and connections to their own experiences.
A Systematic Review: Identifying Research Gaps in Emotional Intelligence Development Using Storybook as a Learning Tool

Emotional Intelligence is defined by its capacity for social success.Children must have the ability to properly assess other individuals or groups and react in an acceptable way.This systematic review intends to fill the knowledge gap on the usage of storybooks as a learning tool for developing emotional intelligence among preschool children.According to the findings, there has been little research into the significance of storybooks as a learning tool for developing emotional intelligence.Furthermore, most of the studies do not use preschool children as a sample.As a result, many of the studies concentrated on the effects on a few emotions, which cannot be considered a true definition of emotional intelligence.According to the findings, more research is needed to determine the benefits of storybooks as a learning tool in developing emotional intelligence in children.Aside from that, findings have revealed the importance of creating emotional intelligence-themed children's storybooks as a learning tool for the development of emotional intelligence in children.
Journal Editorial: “UO Libraries Supports New U.S. Policy Making Research Results Available Immediately”

The letter below was written to the White House Office of Science and Technology Policy in response to its August 2022 memorandum establishing new federal policy on public access to the products of publicly funded research. At the University of Oregon, our students benefit from access to the widest possible range of newly published scholarly information through our Library’s subscriptions and e- resource collections. This access costs millions of dollars annually, much of it paid to the private, for- profit companies who collect and publish research. Many members of the public—including students at less well-funded institutions; high school students and K12 teachers; and curious learners unaffiliated with any university—lack the ability to read and learn from the latest scientific and medical information because the subscriptions are so unaffordable. This lack of access hinders scientific progress and is particularly unfair when considering that the public funds the underlying research activity through their tax dollars. The OSTP’s updated policy will ensure that when the government funds research—by providing grants to buy equipment, materials, hire researchers, run analyses, and more—that the findings from that research are immediately returned to the public for their benefit. We hope that this policy also means that the UO Library will be able to spend less every year on paying for these subscriptions, which will enable us to invest more directly in our students, faculty, and their important teaching and research work. We are proud to support the Oregon Undergraduate Research Journal as an open scholarly communications forum that undergraduates everywhere can read without cost. The UO and its Libraries will continue to advocate strongly for a fair and inclusive research communications system that everyone can afford to participate in.
Pengelolaan Aplikasi Web E-Surat Dapat Meningkatkan Kinerja Pada RSUD Waluyo Jati Kraksaan

Mail management in an organization is important because it will support performance through information fluency, time efficiency, and labor efficiency, This study intends to find out how the management of e-mail Web applications improves performance at Waluyo Jati Kraksaan Hospital. The research used field research with qualitative descriptive methods, data sources from observation, and in-depth interviews with informants, Data credibility test using triangulation technique with relatively the same questions from at least three different sources, with data collection, data reduction, data presentation, and drawing conclusions analysis techniques, The results obtained are that mail management via web e-mail, seen from the aspect of time and labor efficiency, can improve the performance of Waluyo Jati Kraksaan Hospital. Things that need attention for smooth work are the stability of the existing internet system and network.&#x0D; Keywords: Keywords: Management, web e-mail, Application, Performance, and Hospital
DETERMINAN LITERASI DIGITAL MAHASISWA PADA ONLINE LEARNING DI MASA PANDEMIC COVID-19

Nationally, the digital literacy index in Indonesia is still at the "medium" level. During the current pandemic, online learning is required. The purpose of this study is to find the determination of students' digital literacy in online learning?. This research is a descriptive quantitative study with a population of all students at Bina Bangsa University. Data were analyzed by descriptive statistical method. The results of this study indicate that the level of student literacy is not optimal. So that the development of online learning literacy
Retracted: Effective of Smart Mathematical Model by Machine Learning Classifier On Big Data in Healthcare Fast Response

[This retracts the article DOI: 10.1155/2022/6927170.].
Implementation Of SWOT Analysis Of Marketing Information Systems Based Website On Tongs Media

The purpose of this study is to find out and explain the development of information systems as a sales medium at Tongs Media in terms of SWOT analysis. This study uses a qualitative paradigm by using case studies. The findings in this study are the marketing strategy, especially the marketing information system implemented by Tongs Media, which includes several strategies, which include picking up the ball strategy, building a network, providing excellent service, and providing satisfactory service. So that this research can provide a SWOT analysis design for marketing information systems so that researchers can find out and can provide marketing strategies for the future.&#x0D; &#x0D; Keywords: SWOT analysis, Information System, Marketing.
Enhancing Chinese Medical Named Entity Recognition with Auto-Mined Lexicon

Recently, lexicon-based Chinese Named Entity Recognition (NER) models have achieved state-of-the-art performance by benefiting from the rich boundary and semantic information contained in the lexicon. However, in the Chinese medical domain, it’s difficult to obtain the medical lexicon related to the target medical corpus. In this paper, we propose a new paradigm, enhancing Chinese medical NER with Auto-mined Lexicon (ALNER), which alleviates the difficulty of obtaining the medical lexicon by designing a data-driven automatic lexicon construction method. We define medical lexicon construction as a high-quality phrase mining task. We perform secondary annotation on the NER annotated data and use the secondary annotated data to train a deep learning-based phrase tagger. Experimental results show that our method can be combined with different lexicon-based Chinese NER models to improve performance and that the method does not require an external medical lexicon.
Keaksaraan Awal pada Anak Usia Dini: Tinjauan dari Sudut Pandang Orang Tua dan Pendidik

Early literacy for early childhood continues to invite pros and cons. This is what makes parents and educators a dilemma in this study. The purpose of this study is to provide an overview from the point of view of parents and educators on the problem of early childhood literacy. This study invited 6 parents, 6 educators and 1 psychologist to share their point of view through interviews followed by observation for 6 months. Parents are afraid of their children's reading-writing-counting abilities, that is what drives them to include their children in conventional early literacy learning, while educators mix the concepts of playing and conventional as a result of parental demands. Early literacy is not wrongly given to early childhood, what is wrong is the way it is delivered, conveying it with a fun concept, playing while learning. Because basically, children have the right to enjoy their childhood without having to be snatched away by the anxiety of their parents who are their trusted people.
Generating reading comprehension items using automated processes

Over the last five years, tremendous strides have been made in advancing the AIG methodology required to produce items in diverse content areas. However, the one content area where enormous problems remain unsolved is language arts, generally, and reading comprehension, more specifically. While reading comprehension test items can be created using many different item formats, fill-in-the-blank remains one of the most common when the goal is to measure inferential knowledge. Currently, the item development process used to create fill-in-the-blank reading comprehension items is time-consuming and expensive. Hence, the purpose of the study is to introduce a new systematic method for generating fill-in-the-blank reading comprehension items using an item modeling approach. We describe the use of different unsupervised learning methods that can be paired with natural language processing techniques to identify the salient item models within existing texts. To demonstrate the capacity of our method, 1,013 test items were generated from 100 input texts taken from fill-in-the-blank reading comprehension items used on a high-stakes college entrance exam in South Korea. Our validation results indicated that the generated items produced higher semantic similarities between the item options while depicting little to no syntactic differences with the traditionally written test items.
STUDY OF ADAM’S-BASHFORTH PREDICTOR CORRECTOR METHOD FOR SOLVING ORDINARY DIFFERENTIAL EQUATION USING MATLAB

Adam-Bashforth corrector predictor method of ordinary differential equation is practically well suited for solving differential equation efficiently with initial conditions.In this paper we derive a MATLAB code for Adam Bashforth corrector predictor method.The code is extremely efficient for problems with smooth solution as well as problems having rapidly varying solutions.Finally, the proposed method is illustrated by solving an example with help of MATLAB.
An extensible framework for recurrent breast cancer prognosis using deep learning techniques

&lt;span lang="EN-US"&gt;Due to population growth, early illness detection is getting more challenging. Breast cancer is the second-deadliest malignancy. An estimated one million people are newly diagnosed with the disease annually in India. Most cases are never diagnosed because they are either ignored or not reported. Also, secondary malignancies may develop after a breast cancer recurrence, including those of the brain, lungs, and bones. Early detection and treatment of people with recurring breast cancer may help prevent secondary cancers and other disorders. By examining cell and tumour data as well as data from other diseases, this project hopes to overcome this obstacle and more accurately diagnose breast cancer. Accurate diagnosis of breast cancer may be achieved with the use of machine learning techniques. The effort focuses on recurring breast cancer and aims to efficiently identify it. In ensemble learning, decision trees filter out non-essential qualities. Cancer recurrences and non-recurrences are distinguished using voting classifiers. The soft voting classifier classifies a variety of data sets with 98.24% accuracy. The proposed model has an accuracy of 0.97, a recall of 0.97, an F1-Score of 0.969, and a Choen kappa score of 0.9655, as stated by the recommended model.&lt;/span&gt;
Utilization of Digital Media to Restore Reading Interest for University Students

The purpose of this research is to use digital media to rekindle a love of reading among students at the Faculty of Islamic Religion at Unismuh Makassar. Studying how people read with digital media is crucial, as such platforms are now an integral part of most people's lives, and notably those of the digital native millennial generation. Digital media is a type of digital content that can be played by digital media players. Students' behavior and routines have been impacted by the prevalence of digital technology in their lives. Reading books is one activity that has experienced significant change among the younger population, particularly among students. Most students today choose to use search engines on their mobile devices to find the information they need for class. The purpose of this study was to collect comprehensive data on how to rekindle students' love of reading through the creative use of existing digital media. A quantitative strategy was employed in the study's analysis. Students fill out a survey to help gather information. Frequency analysis is used to determine how frequently each answer was provided in the collected data. Parametric analysis is supplemented with descriptive analysis to provide light on what may be done to rekindle a love of reading among Unismuh Makassar students. The findings demonstrate a daily digital media consumption rise, from three to twelve hours on average.
Large dataset partitioning using ensemble partition-based clustering with majority voting technique

&lt;span lang="EN-US"&gt;Large datasets have become useful in data mining for processing, storing, and handling vast amounts of data. However, handling and processing large datasets is time-consuming and memory intensive. As a result, the researchers adopted a partitioning strategy to improve controllability and performance and reduce the time and memory required to handle large datasets. Unfortunately, the numerous clustering techniques available in the literature could confuse experts in choosing the best techniques for a given dataset. Furthermore, no clustering technique can tackle all problems, such as cluster structure, noise, or density. To manage large datasets, existing clustering techniques need scalable solutions. Therefore, this paper proposes an ensemble partition-based clustering with a majority voting technique for large dataset partitioning using the aggregation of k-means, k-medoids, fuzzy c-means, expectation-maximization (EM) and density-based spatial clustering of applications with noise (DBSCAN) techniques. These techniques cluster the large dataset individually in the first stage. The final clusters are discovered in the next stage through a majority voting technique among the five clustering algorithms. These five clustering algorithms assigned data instances to the cluster with the most votes. The experimental findings demonstrate that the ensemble partition-based clustering method surpasses the other five clustering algorithms in terms of execution time and accuracy.&lt;/span&gt;
Instant Graph Neural Networks for Dynamic Graphs

Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes. However, how to instantly represent continuous changes of large-scale dynamic graphs with GNNs is still an open problem. Existing dynamic GNNs focus on modeling the periodic evolution of graphs, often on a snapshot basis. Such methods suffer from two drawbacks: first, there is a substantial delay for the changes in the graph to be reflected in the graph representations, resulting in losses on the model's accuracy; second, repeatedly calculating the representation matrix on the entire graph in each snapshot is predominantly time-consuming and severely limits the scalability. In this paper, we propose Instant Graph Neural Network (InstantGNN), an incremental computation approach for the graph representation matrix of dynamic graphs. Set to work with dynamic graphs with the edge-arrival model, our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Graphs with dynamic structures and dynamic attributes are both supported. The upper bounds of time complexity of those updates are also provided. Furthermore, our method provides an adaptive training strategy, which guides the model to retrain at moments when it can make the greatest performance gains. We conduct extensive experiments on several real-world and synthetic datasets. Empirical results demonstrate that our model achieves state-of-the-art accuracy while having orders-of-magnitude higher efficiency than existing methods.
Optimal Control for Unmanned Systems with One-way Broadcast Communication

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.
Chapter 6. SoundPlay: A Sonic Experience of Digital Loose Parts

The Practices
DETECTING MALICIOUS TWITTER BOTS USING MACHINE LEARNING

In today's world, Twitter is used often &amp; has taken on significance in lives about many individuals, including businessmen, media, politicians, &amp; others. One about most popular social networking sites, Twitter enables users towards share their opinions on a range about subjects, including politics, sports, financial market, entertainment, &amp; more. It is one about fastest methods about information transfer. It significantly influences how individuals think. There are more people on Twitter who mask their identities for malicious reasons. Because it poses a risk towards other users, it is important towards recognise Twitter bots. Therefore, it is crucial that tweets are posted through real people &amp; not Twitter bots. A twitter bot posts spam-related topics. Thus, identifying bots aids in identifying spammessages. Twitter account attributes are used as Features in machine learning algorithms towards categorise users as real or false. In this study, we employed Decision Tree, Random Forest, &amp;Multinomial Naive Bayes as three machine learning methods towards determine if an account was authentic or not. algorithms' accuracy &amp; classificationperformance are compared. Multinomial Naive Bayesmethod has an accuracy about 89%, Random Forest algorithm about 90%, &amp; Decision Tree algorithm about 93%. As a result, it can be seen that Decision tree performs among greater accuracy than Random Forest &amp; Multinomial Nave Bayes.
SENTIMENTAL ANALYSIS OF CUSTOMER PRODUCT REVIEWS USING MACHINE LEARNING

Today, computerized surveys assume a vital part in improving worldwide correspondences among purchasers and impacting buyer purchasing conduct. Online business monsters like Amazon, Flipkart, and so forth give a stage to purchasers to share their experience and give genuine bits of knowledge about the exhibition of the item to future purchasers. To extricate significant experiences from an enormous arrangement of surveys, grouping of surveys into positive and negative opinion is required. Opinion Analysis is a computational report to remove emotional data from the text. In the proposed work, surveys have been characterized into positive and negative opinions utilizing Sentiment Analysis. Out of the different arrangement models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree have been utilized for characterization of audits. The assessment of models is finished utilizing 10 Fold Cross Validation
Experiential Learning in English Language Teaching: A study

In experiential learning, the importance of experience is recognized. The essential function of the learning process is that of the student's experience in experiential learning. Learning by doing, doing one's own thing, and discovering new things are all terms used to describe the process of learning via experience. Experiential learning is both a concept and a practice that encourages teachers to actively engage students in hands-on learning experiences and introspective thinking to improve students' knowledge, skills, and moral values. “An important foundation of English language instruction is based on the idea that students learn best when they work together on a topic or assignment” (Knutson, 2003). It is also significant for teachers to employ experiential learning in their English classes to combine the four language skills into real-world contexts. “In contrast to just hearing about and reading about a subject, experiential learning emphasizes first-hand contact with the material being studied. A physical component is also required for the phenomenon to be fully understood” (Brown, p.291). As Brown said, experiential learning places an emphasis on the psychomotor components of language acquisition by engaging children in physical tasks where the language is submerged and reinforced. As a result of action, learners are encouraged to use a variety of abilities.