Reviewerly/exHarmony huggingface dataset:
Pragmatics Analysis on Students’ Conversation at Grade Eleven of State of Vocational High School 1 Pematangsiantar

This research was intended to find out the kinds of maxim on students’ conversation at grade eleven of State of Vocational High School (SMK Negeri) 1 Pematangsiantar. This research used descriptive qualitative which the data were obtained through students' conversations about expressing opinions. The subject of this research was the students of SMK Negeri 1 Pematangsiantar at grade eleven in the academic year 2023/2024. The instrument of this research was mobile phone, because the classroom interaction flows quickly, mobile phone was needed to help capturing students’ conversation. The data were collected by using the voice recordings and the transcripts. The data were analyzed based on students' conversations, then classified based on the maxims used in the conversation into each type of maxim, the maxims are then calculated to find the dominant maxim. The result of this research showed that the students used maxim of quantity (18,8%), maxim of quality (21,9%), maxim of relation (6,3%) and maxim of manner (18,8%). It showed that the students’ conversation dominant used maxim of quality in their conversation to express opinions.&#x0D; Keywords: Pragmatic Analysis, Maxim on Coversation, Expressing Opinions
Towards an Improved Unsupervised Graph-Based MRI Brain Segmentation Method

Brain disorders are becoming more prevalent, and accurate brain segmentation is a vital component of identifying the appropriate treatment. This study introduces an enhanced graph-based image segmentation technique. The node selection process involves creating an ellipsoid centered at the image’s center of mass. The proposed approach is evaluated using the NFBS dataset and demonstrates superior visual and numerical outcomes compared to some of existing approaches.
4 Clustering and association algorithm

This chapter provides an extensive examination of clustering and association algorithms, offering a comprehensive understanding of these essential unsupervised learning techniques in the field of machine learning. The chapter commences with an introductory section that underscores the significance of unsupervised learning, and presents a detailed overview of clustering and association algorithms. The various clustering algorithms covered in the following sections are k-means, hierarchical clustering, densitybased clustering (like DBSCAN), and Gaussian mixture models. Additionally, the chapter explores evaluation metrics for clustering, including both internal and external evaluation metrics, and offers advise on how to properly interpret the evaluation results. The chapter also examines dimensionality reduction methods, particularly principal component analysis and t-SNE, and clarifies how to use them in clustering scenarios. The Apriori algorithm, frequent itemset mining, association rule development, and methods for judging rule interestingness are all covered in the section on association rule learning. Along with explaining their individual uses, the chapter also goes into advanced clustering methods like density-based spatial clustering, mean-shift clustering, spectral clustering, and affinity propagation. The chapter also discusses association rule mining, ensemble clustering, and time series clustering in huge datasets. The chapter concludes with an overview of the core ideas and developments in unsupervised learning, along with a list of the field’s present research roadblocks and potential future paths.
Deep learning for sub-Nyquist sampling scanning white light interferometry

This Letter introduces sub-Nyquist sampling vertical scanning white light interferometry (SWLI) using deep learning. The method designs Envelope-Deep Residual Shrinkage Networks with channel-wise thresholds (E-DRSN-cw), a network model extracting oversampling envelopes from undersampled signals. The model improves the training efficiency, accuracy, and robustness by following the soft thresholding nonlinear layer approach, pre-padding undersampled interference signals with zeros, using LayerNorm for augmenting inputs and labels, and predicting regression envelopes. Simulation data train the network, and experiments demonstrate its superior performance over classical methods in the accuracy and the robustness. The E-DRSN-cw provides a swift measurement solution for SWLI, removing the need for prior knowledge.
Sistem Informasi Geografis Pemetaan Lokasi Sekolah di Wilayah Kecamatan Wewewa Tengah dengan Menggunakan Metode Arcview GIS

The most crucial area that needs to be addressed in order to raise the standard of human resources is education. Wewewa Tengah sub-district has a vast selection of schools, allowing parents and other young kids to pick one that best matches their needs. Many parents are unaware of where to find information about a school's profile and location, which makes it challenging for parents to select a school for their children. The objective of this research is to create a website-based Geographic Information System that allows for the collecting of school data starting in kindergarten and continuing through middle school, high school, and vocational school. This app encourages users to look up local schools and offers details on each institution's point coordination system. This application is intended to make it simpler for the general public to learn where schools are located and when they start in Central Wewewa. A linear-sequential technique (Waterfall) was utilized to construct the system, and a number of components, including HTML, PHP, and MySQL, were utilised. The key advantage of this study is webgis that can provide school locations and start times in the Central Wewewa community. The study's findings suggest that WebGIS makes it simpler to produce maps, that the system can display locations on a map and information about school locations, and that the system can function effectively.Keywords: Geographic information system, Mapping, WebGIS
Design of Information System for Outpatient Emergency Room Eligibility Letter to Support BPJS Claim Reporting

The advancement of technology in this era has had a significant impact on various fields, including healthcare. Hospitals strive to provide the best service to their patients so that they can get the best solution to their illnesses. A good hospital can be seen from the services provided to its patients. This research aims to produce an information system for creating patient eligibility letters in the emergency department. The method used in this research is a qualitative method with a descriptive approach by conducting data collection stages through interviews, observations, and literature studies, and the method used for developing the device is the System Development Life Cycle (SDLC) method. From this research, several problems were found in processing the creation of patient eligibility letters, namely the creation of eligibility letters still uses claim that has not been bridged with sims, causing the process of creating patient eligibility letters to take a long time, thus affecting the quality of healthcare services in the hospital's emergency unit. The suggestion given is the need for a redesign in the sims system to be bridged with vclaim.
Analisis Sentimen Komentar SIAKAD Menggunakan Metode Naive Bayes Classifier

In all industries, information and communication technology is expanding very quickly. All administrative tasks have ramifications that genuinely call for technology. When the innovation successfully mixes technology with information, the position of this technology becomes increasingly significant. Information technology has permeated so deeply into even the most insignificant aspects of human life, such as the use of academic information systems (SIAKAD) in raising the caliber of academic services, that there are many users who use it in their daily lives who have witnessed an acceleration of change that was previously unimaginable. Public sentiment was categorized and then used to conduct the analysis. The Navie Bayes Classifier is the main technique applied in this study. To assess the degree of accuracy, a comparison will be made using this approach. Positive and negative emotions are classified as sentiments. The purpose of this study is to tell the public about SIAKAD by utilizing student feedback and understanding the degree of accuracy of the techniques examined for method comparison. The test results will undergo method testing and accuracy testing using the Rapidminer tool.Keywords: Navie Bayes Classifier, SIAKAD
Optimisme saat pandemi: strategi storytelling pada video promosi destinasi pariwisata super prioritas

As domestic tourist arrivals begin to grow amid the easing of the Covid-19 pandemic in Indonesia, the Ministry of Tourism and Creative Economy (Kemenparekraf) of the Republic of Indonesia is optimistic about the revival of the tourism sector in 2021. For this reason, the Ministry of Tourism and Creative Economy produces promotional videos for Wonderful Indonesia storytelling style that prioritizes super priority tourist destinations. This study aims to evaluate the use of digital storytelling strategies in promotional video content for Lake Toba, Lombok, Labuan Bajo, and Borobudur. The exploratory sequential mixed methods design was used to obtain qualitative and quantitative data. Based on directed qualitative content analysis using the concept of storytelling heroes journey structure, the four video contents highlight the domination of hero characters in the story structure. However, the most complete content of heroic characters is found in the video for the destination of Borobudur, rather than Lake Toba, which has the highest production costs. Then, based on the results of experimental validation of the findings of previous qualitative data, it was found that the digital storytelling technique used was able to moderately influence the positive image of the four destinations. Digital storytelling for tourism promotion videos in the future needs to integrate the complete structure of heroic stories by involving a variety of supporting characters, both protagonists and antagonists, as well as incorporating elements of economy and pacing in the content
A Large Language Model Screening Tool to Target Patients for Best Practice Alerts: Development and Validation

Best Practice Alerts (BPAs) are alert messages to physicians in the electronic health record that are used to encourage appropriate use of health care resources. While these alerts are helpful in both improving care and reducing costs, BPAs are often broadly applied nonselectively across entire patient populations. The development of large language models (LLMs) provides an opportunity to selectively identify patients for BPAs.In this paper, we present an example case where an LLM screening tool is used to select patients appropriate for a BPA encouraging the prescription of deep vein thrombosis (DVT) anticoagulation prophylaxis. The artificial intelligence (AI) screening tool was developed to identify patients experiencing acute bleeding and exclude them from receiving a DVT prophylaxis BPA.Our AI screening tool used a BioMed-RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach; AllenAI) model to perform classification of physician notes, identifying patients without active bleeding and thus appropriate for a thromboembolism prophylaxis BPA. The BioMed-RoBERTa model was fine-tuned using 500 history and physical notes of patients from the MIMIC-III (Medical Information Mart for Intensive Care) database who were not prescribed anticoagulation. A development set of 300 MIMIC patient notes was used to determine the model's hyperparameters, and a separate test set of 300 patient notes was used to evaluate the screening tool.Our MIMIC-III test set population of 300 patients included 72 patients with bleeding (ie, were not appropriate for a DVT prophylaxis BPA) and 228 without bleeding who were appropriate for a DVT prophylaxis BPA. The AI screening tool achieved impressive accuracy with a precision-recall area under the curve of 0.82 (95% CI 0.75-0.89) and a receiver operator curve area under the curve of 0.89 (95% CI 0.84-0.94). The screening tool reduced the number of patients who would trigger an alert by 20% (240 instead of 300 alerts) and increased alert applicability by 14.8% (218 [90.8%] positive alerts from 240 total alerts instead of 228 [76%] positive alerts from 300 total alerts), compared to nonselectively sending alerts for all patients.These results show a proof of concept on how language models can be used as a screening tool for BPAs. We provide an example AI screening tool that uses a HIPAA (Health Insurance Portability and Accountability Act)-compliant BioMed-RoBERTa model deployed with minimal computing power. Larger models (eg, Generative Pre-trained Transformers-3, Generative Pre-trained Transformers-4, and Pathways Language Model) will exhibit superior performance but require data use agreements to be HIPAA compliant. We anticipate LLMs to revolutionize quality improvement in hospital medicine.
Global schema as local data integrator using active learning to identify candidates attributes

Data integration represents a challenge in application development. Although there are several alternatives to data integration, such as federated and distributed databases, there are still problems with the standardisation of distinct data sources, and this happens because different companies develop distinct systems with different paradigms and concepts. In this paper, we present a case study, in the agriculture and environment domain, of an essential point in the data integration domain which is to show resources to identify nearby attributes concerning the characteristics of the content foreseen in the requirements presented in the proposed schema. Information technology experts in agribusiness help map the most relevant attributes for the investigated scenario. In our experimental tests, we used a quantitative method data analysis approach to validate the results with quantitative comparisons regarding the percentages of proximity between the attribute contents in the databases. Our proposal presents an alternative to simplify data integration without intermediate application or middleware layers. The results were measured on a scale between 0% and 100% to identify candidate attributes. The results were good in identifying attributes in the databases in almost 67% of the cases.
LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.
Impact of YouTube Ads on Consumer Purchase in Bengaluru City

Abstract: This research aims to study the impact of YouTube ads on consumer purchase in Bengaluru. The research collected data from 80 respondents using a survey method. The data were analyzed using descriptive statistics and the chi-square test. The results showed that YouTube ads have a positive impact on consumer purchase in Bengaluru. The majority of the respondents watch YouTube daily, and a significant number of them watch YouTube ads. Moreover, YouTube ads influence their purchase decision, and a considerable number of respondents have made a purchase after watching a YouTube ad. Therefore, businesses can leverage YouTube ads to reach their target audience and increase their sales.
On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications

The integration of Deep Learning (DL) models with the HoloLens2 Augmented Reality (AR) headset has enormous potential for real-time AR medical applications. Currently, most applications execute the models on an external server that communicates with the headset via Wi-Fi. This client-server architecture introduces undesirable delays and lacks reliability for real-time applications. However, due to HoloLens2’s limited computation capabilities, running the DL model directly on the device and achieving real-time performances is not trivial. Therefore, this study has two primary objectives: (i) to systematically evaluate two popular frameworks to execute DL models on HoloLens2—Unity Barracuda and Windows Machine Learning (WinML)—using the inference time as the primary evaluation metric; (ii) to provide benchmark values for state-of-the-art DL models that can be integrated in different medical applications (e.g., Yolo and Unet models). In this study, we executed DL models with various complexities and analyzed inference times ranging from a few milliseconds to seconds. Our results show that Unity Barracuda is significantly faster than WinML (p-value &lt; 0.005). With our findings, we sought to provide practical guidance and reference values for future studies aiming to develop single, portable AR systems for real-time medical assistance.
The Correlation Between Motivation and Self-Care Mangement Among Leukemia Patients Based on Orem's Theory

Introduction: Leukemia is one of the most diagnosed types of cancer and a cause of death worldwide. Patients who experience severe side effects of cancer treatment tend to experience less than optimal self-care management. Leukimia patients in treatment often complain of fatigue, weakness, lack of energy, and nausea which hinder them in self-care management. The purpose of this study was to determine the relationship between motivation and self-care management. Methods: This study used an analytic descriptive research design with a cross sectional approach. Population on this research were members of Chronic Granulocytic Leukemia (it is known as “ELGEKA”), East Java, Indonesia. The sample size was 69 respondents using purposive sampling. The independent variable was motivation and the dependent variable was self-care management. Data was analyzed using Spearman Rho analysis with level of significance 0.05. Results: The results of this study indicated that there was a relationship between motivation and self-care management (p=0.000) and (r=0,531). Discussion: Motivation has a significant relationship with self-care management in leukemia patients based on Orem's theory. The higher the motivation, the higher self-care management. Good self-care management involves nutrition and fluid management, physical activity, stress management, complementary therapy, information and support seeking, and medication adherence. Conclusion: Nurses have an important role in identifying and increasing patient motivation to carry out self-care management and changing their attitudes to improve health and disease care. The recommendation for future researchers is to develop research with interventions to improve self-care management in leukemia patients.
A Study on the Effect of Family Capital on College Students Academic Achievement

The relationship between family socioeconomic status and students academic achievement has long been a topic of intense academic interest; however, few studies have combined family economic capital, cultural capital, and social capital as family capital to investigate its influence on students academic achievement and how it affects students academic achievement. This study investigates the relationship between family capital and the mechanisms of influence on students academic achievement in an effort to generate new strategies for enhancing family capital and students academic achievement. Using the method of literature review, it was determined that the social, cultural, and economic capital of students families were all substantially related to their academic achievement in school, and the greater the family capital, the higher the students academic achievement. In this process, academic goals play a mediating function, meaning that family capital not only directly affects students academic achievement, but also indirectly affects it by influencing their academic goals.
Developing responsible AI practices at the Smithsonian Institution

Applications of artificial intelligence (AI) and machine learning (ML) have become pervasive in our everyday lives. These applications range from the mundane (asking ChatGPT to write a thank you note) to high-end science (predicting future weather patterns in the face of climate change), but, because they rely on human-generated or mediated data, they also have the potential to perpetuate systemic oppression and racism. For museums and other cultural heritage institutions, there is great interest in automating the kinds of applications at which AI and ML can excel, for example, tasks in computer vision including image segmentation, object recognition (labelling or identifying objects in an image) and natural language processing (e.g. named-entity recognition, topic modelling, generation of word and sentence embeddings) in order to make digital collections and archives discoverable, searchable and appropriately tagged. A coalition of staff, Fellows and interns working in digital spaces at the Smithsonian Institution, who are either engaged with research using AI or ML tools or working closely with digital data in other ways, came together to discuss the promise and potential perils of applying AI and ML at scale and this work results from those conversations. Here, we present the process that has led to the development of an AI Values Statement and an implementation plan, including the release of datasets with accompanying documentation to enable these data to be used with improved context and reproducibility (dataset cards). We plan to continue releasing dataset cards and for AI and ML applications, model cards, in order to enable informed usage of Smithsonian data and research products.
The Legal Path of Managing the Crime of Money Laundering in Digital Currency

The characteristics of decentralized anonymity of digital currency reduce todays financial crimes, especially the cost of money laundering crimes. Due to the relatively slow progress of Chinese research on digital currency, falling behind the current expansion trend of digital currency, there are many difficulties in governing digital currency crime. Therefore, the regulation of digital currency money laundering crime should start from the aspects of clarifying the legal attribute, introducing the standard judicial interpretation, and so on. The explosive growth of digital currency in 2017 has greatly reduced the cost of financial crimes of digital currency, seriously jeopardizing the economic order and peoples property security. Money laundering of digital currency is particularly rampant. Therefore, legal means should be adopted to regulate the application of digital currency in life.
Analysis of the Application of Statistics in Education

Today, most people think that there is no use of statistics in education, a subject that does not seem to be directly related to data, or does not understand the use of statistics in education. This paper will introduce the application of statistics in three different aspects of education. Educational statistics is an important tool in educational management, educational decision-making and educational reform. People can use statistical methods to improve educational management and improve the quality of education, make better educational decisions and promote educational reform.