Reviewerly/exHarmony huggingface dataset:
The Effect of Paddressing and Scanning Training on the Skills of PMR Students in MAN 1 Pohuwato

Emergency conditions can occur anywhere and anytime. However, it does not rule out the possibility of emergency conditions occurring in areas that are not covered by health workers. The purpose of this study was to determine the effect of dressing and scanning training on the skills of PMR MAN 1 Pohuwato students. This research method is quantitative research that uses the Two Group Pretest Posttest design. The population in this study was 22 students and the sample was 22 students. From the results of statistical tests on the application of dressing and scanning in the Experimental group of students, a p-value value of 0.003 was obtained, and the control group had a p-value of 1,000. Dressing and scanning training has an influence on students' skills which can be seen from the improvement of students' skills before and after being given dressing and scanning training in the experimental group, while for the control group, there is no change in skills, so it can be concluded that there is an influence of dressing and scanning training on students' skills. Suggestions for schools to conduct splint wrap training to improve students' skills.
The Relationship of Online Game Addiction with Aggressive Behavior for Teenagers at SMP Negeri 3 Banyumas

There is a lot of verbal and physical aggression in online games today. Playing games with a high level of violence can lead to an increase in hostile attitudes and actions. The purpose of this study was to determine whether there is a relationship between online game addiction and the aggressive behavior of grade VIII students at SMP Negeri 3 Banyumas. This study is a correlational study using a cross-sectional approach. The population in this study consisted of 92 grade VIII students of SMP Negeri 3 Banyumas who played online games. A sample of 75 respondents was taken using a simple random sampling technique. The questionnaire was used as a data collector and analyzed with the Spearman rank correlation test. The results showed that most online game addicts were in the mild category (80%), most showed moderate aggressive behavior (62.7%), and there was a strong relationship between online game addiction and aggressive behavior class VIII SMP Negeri 3 Banyumas with a p-value of 0.000 (p-value &lt;0.05; r = 0.579). This indicates that online game addiction can affect a person's aggressive behavior, the higher the level of online game addiction, the more aggressive behavior it leads. This research is expected to be a reference for pediatric nursing.
Indonesian University Students’ Perceptions on the Use of Social Media for English Language Learning

The use of social media is not only for communicating and enjoying leisure time but also for learning many things. Since that, English-related content has been one of the things that often be on their search. Various educative content can be found on social media. This study aims to find out the students’ perceptions on the use of social media for English language learning. The participants of this study are 98 university students in Jakarta. A descriptive qualitative method was used. The data was obtained by using a questionnaire and interview. The results showed that Instagram, YouTube, and TikTok are the most preferred platform for learning. The students have positive perceptions on the use of social media for English learning because social media is easy to access, fun, stress-free, and reinforces their independence. In addition, the most skill that can be learned from social media are reading.
Implikasi Etika pada Penggunaan Artificial Intelligence (AI) dalam Akuntansi Manajemen

Artificial Intelligence (AI) has changed the way humans work, interact and manage data. One part of accounting work that is influenced by AI is management accounting. There is a gap between the demands for developing the capabilities of accountants in the AI era and the development of management accounting itself. The application of AI in the context of management accounting also raises several ethical issues that need to be considered. This research method uses a qualitative descriptive approach with an analysis of previous research literature being explored and explained. The research results suggest that it is important for future policymakers to ensure standardization of AI systems within the accounting paradigm to ensure high-quality systems that adhere to accounting principles and ethics. To address emerging ethical issues, it is important for organizations to involve ethicists and management accounting experts in the AI development and deployment process. This will help ensure that decisions made by AI systems adhere to widely accepted values and principles. This research provides novelty by filling a gap in the management accounting literature, exploring how AI can influence ethical practices in management accounting
Precursory Signals to Injection Induced Fault Reactivation in the Laboratory Using Active Ultrasonic Monitoring Methods

Abstract Induced earthquakes are still highly unpredictable, and often caused by variations in pore fluid pressure. Monitoring and understanding the mechanisms of fluid‐induced fault slip is essential for seismic risk mitigation and seismicity forecasting. Fluid‐induced slip experiments were performed on critically stressed faulted sandstone samples, and the evolution of the actively sent ultrasonic waves throughout the experiment was measured. Two different fault types were used: smooth saw‐cut fault samples at a 35° angle, and a rough fault created by in situ faulting of the samples. Variations in the seismic slip velocity and friction along the fault plane were identified by the coda of the ultrasonic waves. Additionally, ultrasonic amplitudes show precursory signals to laboratory fault reactivation. Our results show that small and local variations in stress before fault failure can be inferred using coda wave interferometry for time‐lapse monitoring, as coda waves are more sensitive to small perturbations in a medium than direct waves. Hence, these signals can be used as precursors to laboratory fault slip and to give insight into reactivation mechanisms. Our results show that time‐lapse monitoring of coda waves can be used to monitor local stress changes associated with fault reactivation in this laboratory setting of fluid‐induced fault reactivation. This is a critical first step toward a method for continuous monitoring of natural fault zones, contributing to seismic risk mitigation of induced and natural earthquakes.
Sistem Informasi Manajemen dalam Data Pegawai Pendidikan

This study aims to find out how the benefits of management information systems in the knowledge of employee data. This research is library research. The results of the study conveyed that for knowledge, the data processing process functions as an executor of arithmetic, logical processes for data, storing and processing data programs, taking data input programs so that they can be used at any time, minimizing human labor, and getting more accurate results and minimal errors. The function of the database is to group data to facilitate data identification, the database prepares data in accordance with user requests for information quickly and accurately. Employee data has important value for organizations because it can be used to make informed decisions and improve operational efficiency. Good data can be organized in a database. The benefit of using a management information system is that it makes it easier for management to plan, direct, and supervise the course of business.
Photovoltaic output power estimation model based on multi-meteorological variable dimension reduction and improved variational mode decomposition algorithm

The instability and intermittence of photovoltaic (PV) power generation challenge the large-scale integration of PV power generation systems into the power grid and the development of energy storage systems. To accurately predict the output power of PV power generation, a power prediction model based on the optimized variational modal decomposition (OVMD)-adaptive t-distribution sparrow search algorithm (tSSA)-least squares support vector machine (LSSVM) algorithm was built. The performance of the model was then verified by three-year meteorological data and real-time PV output power data of two different climatic regions in China. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of this model were less than 3.1% and 0.40, respectively, and the determination coefficient (R2) of this model was greater than 95%. Moreover, the prediction accuracy was greatly improved compared to models based on the support vector machine (SVM), LSSVM, variational modal decomposition (VMD)-LSSVM, and VMD-SSA-LSSVM algorithms. Finally, by performing dimension reduction of meteorological variables related to PV output power, the performance of the proposed OVMD-tSSA-LSSVM model was further improved.
Hybrid blockchain-based many-to-many cross-domain authentication scheme for smart agriculture IoT networks

With the development of smart agricultural Internet of Things (IoT) projects, the need for extensive collaboration among agricultural devices from different domains has surged, necessitating the authentication of device identities for secure communication. Existing centralized architecture-dependent authentication mechanisms encounter issues like a sole point of failure and inefficiencies. Most authentication schemes are unable to support plentiful devices synchronously connecting to numerous data servers in other domains. To address these problems, we propose a many-to-many cross-domain authentication scheme based on the hybrid blockchain architecture for smart agriculture IoT networks. The scheme enables multiple devices simultaneously executing mutual authentication with several data service providers from other agricultural systems. This paper designs a groupable batch verification (GBV) algorithm that dynamically adjusts the batch size by performing group verification for a list of requests, enhancing the flexibility of cross-domain batch authentication. Furthermore, the proposed scheme provides a pseudonym update mechanism to protect the privacy of devices and guards services of different domains from illegal access by tracking malicious devices. The security analysis and performance evaluation demonstrate that the proposed scheme has superior security features and performance.
Disparities in Electronic Health Record Portal Activation Among Non-primary English-speaking Patients with Food Allergy

Health disparities in food allergy are prevalent among minority and socioeconomically disadvantaged populations. Understanding of healthcare utilization among non-primary English-speaking patients with food allergy is limited. A retrospective review was conducted of patients 0-18 years old with food allergy seen in our institution’s allergy clinic between 1/1/2018-12/31/2022. Only patients with prior history of symptoms consistent with IgE-mediated reaction to food(s) and positive skin testing (wheal 3mm or greater) or food-specific IgE > 0.35 were included. Chi-squared tests and logistic regression (adjusted for age, sex, insurance, race and ethnicity, and median household income of zip code) examined the association between patient/guardian-reported primary language and healthcare utilization measures. Among the 1,418 patients included, 1,267 (89.4%) were primary English speakers and 151 (10.6%) were non-primary English speakers. Other primary languages included Spanish (8.4%), Portuguese (0.5%), Arabic (0.4%), Chinese (0.2%). Non-primary English speakers had significantly higher rates of Medicaid coverage (85% vs 34%, p=0.000) and higher odds of asthma (aOR=0.53, 95% CI 0.32-0.88, p=0.014) as compared to English speakers. Non-primary English-speaking patients had significantly lower odds of electronic health record (EHR) portal activation (aOR=0.48, 95% CI 0.32 – 0.74, p=0.001) than primary English speakers. No significant differences were found in the age at initial visit, total number of clinic visits, odds of having a follow up appointment or an oral food challenge between the two groups. Disparities in rates of EHR portal activation were identified among non-primary English-speaking patients. This study highlights a potential area for improvement in increasing healthcare accessibility for non-primary English-speaking patients.
Analysis Sentiment to Religious Based Issues Netizen Comments on President Jokowi 's Instagram using the Naive Bayes Classifier Method

&#x0D; &#x0D; &#x0D; &#x0D; The presence of Instagram is one of the most popular social media platforms. Make people make a habit of posting photo and video content. In this way, Instagram can become a source of public opinion and sentiment data, which data can be used for social studies. Social studies is still a crucial issue for countries today, including Indonesia, is a matter of religion. Apart from problems regarding religion, public sentiment can be researched from Instagram. The sentiment dataset related to religion is taken from public comments from President Joko Widodo's content regarding Merry Christmas Greetings and Visits to Churches. The analysis system will use sentiment analysis using a method from machine learning, namely the naive Bayes classifier to determine positive &amp; negative sentiments. The comment dataset is 2,600 in which the positive and negative sentiment class datasets are determined manually by experts in the field of linguistics or communication totaling 1,309 to be used as training data. the results of testing the Naïve Bayes classifier training data with a total of 1309 obtained an accuracy prediction of 97.63% and test data where positive &amp; negative sentiment classes had not been determined before hand amounted to 1291 in sentiment analysis obtained positive sentiment 70.56% &amp; negative sentiment 29.44%.&#x0D; &#x0D; &#x0D; &#x0D;
PROTOTYPE METHOD PADA APLIKASI SCHEDULER BERBASIS MOBILE

PT United Tractors is currently implementing digitalization in its various activities. In its implementation, delivering information and archiving documents in the PSD4 Service Division still have obstacles due to the lack of effective and efficient access to information needed to do work. To overcome these problems, the solution that can be provided is to develop a mobile-based document filing system using the Flutter framework to maximize the development of mobile-based systems. For developers to interact directly with teams related to application capabilities, this system development method uses the Prototype method, which has 5 phases: Communication, Quick Plan, Modelling Quick Design, Construction of Prototype, and Development, Delivery, and feedback phases. The result of this research is an Android-based Scheduler Mobile document archiving application that has been made based on the steps in the prototype method. Testing using Black Box and Flutter DevTools is done to find out the results of the application that has been made. Black Box and Flutter DevTools testing results show that the application can run as expected.
ANALISIS SISTEM INFORMASI AKUNTANSI PENGGAJIAN PADA PT ALFA KREASI (WEEKEND INC)

This writing aims to determine the application of the Payroll Accounting Information System carried out at PT Alfa Kreasi (WEEKEND INC), as well as to analyze the implementation of the Internal Control System and provide proposals for the Payroll Accounting System to support the effectiveness and efficiency of the Internal Control System at PT Alfa Kreasi (WEEKEND INC ). The type of data used is qualitative data, data that explains a phenomenon based on things that generally contain words where the data obtained and which will be processed later is in the form of primary data, which is obtained directly through face-to-face interviews with the parties concerned. The analytical tools that the author uses in this research are data flow diagrams (DFD) and document flow charts (flowcharts). From the research that has been carried out, the author concludes that PT Alfa Kreasi (WEEKEND INC) implements a computerized payroll system seen in the recording process carried out on Google Sheets and for all data regarding payroll in the process of paying employee salaries via bank transfer. And the analysis of the company's Internal Control System has also been implemented in accordance with the policies set by the leadership.
Effect of Employee Training on Organizational Performance in Public Sector in Rwanda. A Case Study of Rwanda Social Security Board (RSSB)

This paper examined the effect of training on the performance of public-sector organizations using the Rwanda Social Security Board as a case study. The research approach design used in this study was descriptive, and a total of 130 questionnaires were distributed to respondents, of which 108 were fully completed and returned, and 10 interviews were conducted. The overall response rate was 90.7%. The sampling techniques used were simple random and purposive samplings. The analysis used both descriptive and inferential statistics of Pearson Correlation. Qualitative data was examined and categorized into themes, while quantitative data was coded and entered into the SPSS package.&nbsp; The study found that there is a significant relationship between training and organizational performance. The first statistic, R, is .908a, indicating that there is a great deal of variance shared by the independent variables and dependent variables. The R Square value of 0.824 indicates that approximately 82.4% of the variability in the outcome variable can be explained by the predictors in the model. The unstandardized coefficients for the predictor variables show their impacts on organizational performance at the Rwanda Social Security Board. Specifically, Training regulations have a coefficient of (β= 0.285, t=5.050, p value=0.000), and types of Training have a coefficient of (β= 0.123, t=2.028, p value=0.045). The coefficients are statistically significant on Organizational Performance at Rwanda Social Security Board, as indicated by their associated Sig. Values below 0.05. Based on the findings, the study recommends that the Rwanda Social Security Board conduct training needs assessments before recommending training for its employees and establish a transparent training policy that provides equal opportunities in training Keywords: Employee training, types of training, training regulations and organizational performance
Can we Constrain Concept Bottleneck Models to Learn Semantically  Meaningful Input Features?

Concept Bottleneck Models (CBMs) are considered inherently interpretable because they first predict a set of human-defined concepts before using these concepts to predict the output of a downstream task. For inherent interpretability to be fully realised, and ensure trust in a model's output, we need to guarantee concepts are predicted based on semantically mapped input features. For example, one might expect the pixels representing a broken bone in an image to be used for the prediction of a fracture. However, current literature indicates this is not the case, as concept predictions are often mapped to irrelevant input features. We hypothesise that this occurs when concept annotations are inaccurate or how input features should relate to concepts is unclear. In general, the effect of dataset labelling on concept representations in CBMs remains an understudied area. Therefore, in this paper, we examine how CBMs learn concepts from datasets with fine-grained concept annotations. We demonstrate that CBMs can learn concept representations with semantic mapping to input features by removing problematic concept correlations, such as two concepts always appearing together. To support our evaluation, we introduce a new synthetic image dataset based on a playing cards domain, which we hope will serve as a benchmark for future CBM research. For validation, we provide empirical evidence on a real-world dataset of chest X-rays, to demonstrate semantically meaningful concepts can be learned in real-world applications.
Institutional Platform for Secure Self-Service Large Language Model  Exploration

This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make large, customized language models (LLMs) more accessible. By capitalizing on recent advancements in multi-LoRA inference, the system efficiently accommodates custom adapters for a diverse range of users and projects. The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. We illustrate the establishment of a tenant-aware computational network using agent-based methods, securely utilizing islands of isolated resources as a unified system. The platform strives to deliver secure LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication. This contribution aligns with the overarching goal of enabling simplified access to cutting-edge AI models and technology in support of scientific discovery.
Useful entanglement can be extracted from noisy graph states

Cluster states and graph states in general offer a useful model of the stabilizer formalism and a path toward the development of measurement-based quantum computation. Their defining structure -- the stabilizer group -- encodes all possible correlations which can be observed during measurement. Those outcomes which are compatible with the stabilizer structure make error correction possible. Here, we leverage both properties to design feasible families of states that can be used as robust building blocks of quantum computation. This procedure reduces the effect of experimentally relevant noise models on the extraction of smaller entangled states from the larger noisy graph state. In particular, we study the extraction of Bell pairs from linearly extended graph states -- this has the immediate consequence for state teleportation across the graph. We show that robust entanglement can be extracted by proper design of the linear graph with only a minimal overhead of the physical qubits. This scenario is relevant to systems in which the entanglement can be created between neighboring sites. The results shown in this work may provide a mathematical framework for noise reduction in measurement-based quantum computation. With proper connectivity structures, the effect of noise can be minimized for a large class of realistic noise processes.
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with  Contrastive Learning in Multimodal Electronic Health Records

In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial. Electronic Health Records (EHRs) have become integral to medical decision-making, yet existing methods for enhancing model fairness restrict themselves to unimodal data and fail to address the multifaceted social biases intertwined with demographic factors in EHRs. To mitigate these biases, we present FairEHR-CLP: a general framework for Fairness-aware Clinical Predictions with Contrastive Learning in EHRs. FairEHR-CLP operates through a two-stage process, utilizing patient demographics, longitudinal data, and clinical notes. First, synthetic counterparts are generated for each patient, allowing for diverse demographic identities while preserving essential health information. Second, fairness-aware predictions employ contrastive learning to align patient representations across sensitive attributes, jointly optimized with an MLP classifier with a softmax layer for clinical classification tasks. Acknowledging the unique challenges in EHRs, such as varying group sizes and class imbalance, we introduce a novel fairness metric to effectively measure error rate disparities across subgroups. Extensive experiments on three diverse EHR datasets on three tasks demonstrate the effectiveness of FairEHR-CLP in terms of fairness and utility compared with competitive baselines. FairEHR-CLP represents an advancement towards ensuring both accuracy and equity in predictive healthcare models.