Reviewerly/exHarmony huggingface dataset:
Learning Model with LMS as an Innovation to Facilitate the Implementation of Learning from Home

Learning from home occurs due to the Covid-19 pandemic. Even if it is done from home, learning quality must be maintained. This study aims to analyze the effect of combining learning models with LMS to increase student learning independence and outcomes. This research combines LMS media, in this case, Schoology, blended learning model, and guided inquiry learning model in one package. The study used a pretest and posttest control group design. The control group used google classroom and google meet. The experimental group used a combination of the learning model with LMS. It was found that the average quality of the learning independence of the experimental group was higher than the mean of independence in the learning of the control group. Likewise, the learning achievement of the experimental group obtained a value greater than the average learning achievement of the control class. The combination of learning models with LMS effectively increases independence in learning and student learning outcomes in learning situations from home.
Trie-based Output Space Itemset Sampling

Pattern sampling algorithms produce interesting patterns with a probability proportional to a given utility measure. Utility changes need quick repreprocessing when sampling patterns from large databases. In this context, existing sampling techniques require storing all data in memory, which is costly. To tackle these issues, this work enriches D. Knuth’s trie structure, avoiding 1) the need to access the database to sample since patterns are drawn directly from the enriched trie and 2) the necessity to reprocess the whole dataset when utility changes. We define the trie of occurrences that our first algorithm TPSpace (Trie-based Pattern Space) uses to materialize all of the database patterns. Factorizing transaction prefixes compresses the transactional database. TPSampling (Trie-based Pattern Sampling), our second algorithm, draws patterns from a trie of occurrences under a length-based utility measure. Experiments show that TPSampling produces thousands of patterns in seconds.
Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

Objectives: We used electronic health record (EHR)–based raw audit logs to classify the work settings of anesthesiology physicians providing care in both surgical intensive care units (ICUs) and operating rooms. Study Design: Observational study. Methods: Attending anesthesiologists who worked at least 1 shift in 1 of 4 surgical ICUs in calendar year 2019 were included. Time-stamped EHR-based audit log events for each week were used to create event frequencies and represented as a term frequency–inverse document frequency matrix. Primary classification outcome of interest was a physician’s clinical work setting. Performance of multiple supervised machine learning classifiers were evaluated. Results: A total of 24 attending physicians were included; physicians performed a median (IQR) of 2545 (906-5071) EHR-based actions per week and worked a median (IQR) of 5 (3-7) weeks in a surgical ICU. A random forest classifier yielded the best discriminative performance (mean [SD] area under receiver operating characteristic curve, 0.88 [0.05]; mean [SD] area under precision-recall curve, 0.72 [0.13]). Model explanations illustrated that clinical activities related to signing of clinical notes, printing handoff data, and updating diagnosis information were associated with the positive prediction of working in a surgical ICU setting. Conclusions: A random forest classifier using a frequency-based feature engineering approach successfully predicted work settings of physicians with multiple clinical responsibilities with high accuracy. These findings highlight opportunities for using audit logs for automated assessment of clinician activities and their work settings, thereby affording the ability to accurately assess context-specific work characteristics (eg, workload).
Libraries of Ukraine as Centers of the National Resistance

The relevance of the article. Ukrainian society is experiencing a difficult period of existence, in 2022 began a full-scale Russian aggression against our country, which aggravated many complex issues of physical survival and psychological endurance of Ukrainians. Current life of Ukrainian citizens is characterized by instability, threats of physical destruction, information disorientation, unpredictability, the need to simultaneously solve many vital problems, which is a significant stress factor. Under these conditions, Ukrainian librarians, despite the destruction of many library premises, actively engaged in helping the society in all possible directions.&#x0D; The purpose of the article. To prove the availability of information and resource potential of public libraries of Ukraine as centers of national resistance through diversification of their basic functions, attraction of innovative instruments of activity and channels of communication.&#x0D; The methodology. The article considers the activity of Ukrainian libraries as centers of national resistance and psychological support of the society during the Russian military aggression against Ukraine.&#x0D; The results. The article presents an innovative view on the content and organization of the basic directions of socio-cultural activity of Ukrainian libraries. The need to strengthen the information impact on users through social networks, use of communication potential of copywriting and Storytelingu was emphasized. The availability of powerful capabilities and reserves of critical thinking, media literacy and information hygiene skills for libraries has been proved.&#x0D; The scientific novelty. The article presents an innovative view on the content and organization of the basic directions of socio-cultural activity of Ukrainian libraries.&#x0D; The practical significance. The author analyzes the activity of Ukrainian libraries as centers of national resistance and psychological support of the society during the Russian military aggression against Ukraine.
System for Security and Prevention of Terrorism Attacks at MRT Lebak Bulus, Jakarta

Public transportation systems such as the Lebak Bulus MRT are likely to become prime targets for terrorists due to the potential for disruption, destruction, and possible escape of perpetrators, due to size, openness, accessibility, lack of identification of passengers, and the number of people carried by the transportation system. Risk prevention and preparedness in the Lebak Bulus MRT transportation system is critical to internal security and requires proper analysis of asset vulnerabilities, clear awareness of criticality, possible countermeasures, and adequate methods to design, scale and optimize protection. This study uses a literature study research method. The author uses previous research sources that are relevant to the topic under study, especially related to the security system and prevention of terrorism attacks at MRT Lebak Bulus Jakarta. CPTED is a complex strategy that will include a variety of interventions and behavioral responses from different groups of users. CPTED involves territoriality, surveillance, access control, activity support, image/management, and target setting. In the Mass Rapid Transportation (MRT) Lebak Bulus, the vulnerability to terrorist attacks can be identified based on physical security theory. The main vulnerability is the MRT central depot. All MRT control is in the central depot, not on the mechanics. The MRT driver is only in charge of opening and closing the train doors, so that the control of the MRT, which is located at the Lebak Bulus central depot, places the vulnerability to hijackings by terrorists.
Improving Convolution via Cache Hierarchy Tiling and Reduced Packing

Convolution is one of the most computationally intensive machine learning model operations, usually solved by the known Im2Col + BLAS method. This work proposes a novel convolution-algorithm to improve upon Im2Col + BLAS by introducing (a) CSA: a convolution specific 3D cache-blocking analysis that focuses on tile reuse over the cache hierarchy, (b) CSO: a macro-kernel that follows CSA to compute the convolution by tiling it, (c) a specialized microkernel that seeks to achieve peak hardware performance, and (d) packing routines for the input tensor and filters to bridge the gap between tiling and micro-kernel. Our approach speeds up end-to-end machine learning model inference by up to 26% and 21% for x86 and POWER10 architectures, respectively.
A Visual Diagnostic Tool for Causal Inference

Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationships between confounders and outcomes are sufficiently controlled, or whether there is a more complex relationship that requires further adjustment.This short commentary highlights this simple tool, providing an example of its utility along with relevant R code.
Computational Pathology for Brain Disorders

Abstract Noninvasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of computational pathology has triggered great hope to ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-the-art machine learning techniques used to analyze whole slide images within the context of brain disorders. We present a selective set of remarkable machine learning algorithms providing discriminative approaches and quality results on brain disorders. These methodologies are applied to different tasks, such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing morphological phenotypes for classification and quantitative assessment of disease, improving clinical care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.
A Phenomenological Study on the Experience of College of Education Students Who Participated in the Semester Practicum

본 연구는 학기제 교육실습에 참여한 사범대학생들의 경험을 현상학적으로 살펴보는 데 목적이 있다. 이를 위해 사범대학생 중에서 학기제 교육실습에 참여한 11명을 대상으로 이들의 경험을 분석하였다. 연구방법으로는 Giorgi의 현상학적 방법을 적용하였으며 5개의 주제, 16개의 하위구성요소, 152개의 의미단위들이 도출되었다. 본 연구에서 학기제 교육실습의 경험은 배움이 있는 교육실습 기대, 사회적 관계 경험, 포화상태로 변하는 학업과 실습의 병행, 전문성 신장을 위한 노력, 실습을 끝냈다는 뿌듯함과 미래에 대한 고민으로 도출되었다. 연구참여자들은 교육실습 중 수업기술 부족으로 인하여 학생 통제 및 수업 준비 과정에서 어려움을 느끼고 있으나, 교사 직무 및 행정 업무를 비롯하여 학교교육과정의 다양한 운영을 경험하였다. 또한 교사로서의 태도와 책임감, 교직에 대한 가치도 갖게 되었다. 본 연구는 사범대학생의 학기제 교육실습 경험을 이해하고 이들에 관한 사회적, 정책적 관심을 높이고자 했다는 데 의의가 있다고 본다.This study explores phenomenologically the experiences of college of education students who participated in the semester practicum. In this study, we analyzed the experiences of 11 students who participated in the semester practicum. From the phenomenological research method of Giorgi, this study found five themes, 16 categories, and 152 semantic units through in-depth interviews. The five dimensions are 1) the expectation of educational practice, 2) the experience of social relationships, 3) the parallel study and practice that is becoming saturated, 4) the effort to enhance professionalism, and 5) the pride of having completed practice and worriedness about the future. The participants felt difficulties controlling students and preparing for classes because of a lack of instructional skills during educational practice. However, they understood the duties of teachers, including teachers' duties and administrative tasks. In addition, they experienced school curriculum implementation, a teacher's attitude and responsibility, and the teaching profession's value. This study is meaningful because it understands the experience of semester practicum and raises social and policy interest in them.
A Literature Review on Segmentation of Medical Images using U-Net and Other Techniques

Image segmentation's digital image processing subfield has numerous applications in image analysis. The growing science of medical image analysis has made it difficult to segment regions, disorders, or anomalies in medical imaging. Monitoring the growth of conditions like plaque and tumors is made easier with the help of medical image segmentation. Areas of interest (ROIs) from the dataset are extracted as a component of medical image segmentation. such as that from ultrasound imaging (US). Medical image segmentation can take a long time, but recent advancements in software techniques like Deep Learning, Machine Learning, and Artificial Intelligence (AI) are making routine tasks easier to complete. so that it makes it possible to analyze anatomical data with greater precision by separating out only the essential regions. The segmentation of intima carotids and media on an ultrasound image is crucial for reducing the annual number of deaths caused by atherosclerosis. Plaque, or the accumulation of fats, cholesterol, and other substances on and within the artery walls, is known as atherosclerosis. Recently, a number of image segmentation tasks have implemented deep neural network models. In this survey, we look at the previous research that a number of authors have done on segmentation using a variety of methods, such as the U-net (U-Net architecture was developed with minimal changes to CNN architecture) and its variants. For better understanding, we have also studied a few more techniques of Segmentation in addition to U-net. Key Words: Intima media, Atherosclerosis, U-Net, Segmentation, carotid artery, ultrasound imaging.
THE EFFECT OF INTEGRATED SCIENCE WORKSHEETS CONTAINING NEW LITERACY THEME OF RESPIRATORY HEALTH AND EXCRETION ON THE STUDENT’S COMPETENCE IN SMPN 25 PADANG

In the 2013 curriculum, science learning was implemented based on integration to improve student competency. However, the real conditions in schools show that the integration of learning materials in the worksheet and literacy integration that were applied was in the low category. The solution to overcome this problem is to use a integrated science worksheet containing new literacy theme of respiratory health and excretion. The purpose of this research was to investigate the effect of integrated science worksheet containing new literacy theme of respiratory health and excretion on the students competence in SMPN 25 Padang. This type of research used quasi experimental design with randomized control group posttest only design. The instrument used in this research consisted of observation sheet for attitude competency, written test for knowledge competency, and performance assessment for skill competency. Based on the analysis that has been done, it can be stated that the use of integrated science worksheet containing literacy new has a significant influence on student competence. These results indicate that the use of integrated science worksheet containing new literacy theme of respiratory health and excretion is effectively applied in schools.
Establishment of Performance Evaluation System for Major Curriculum: Focusing on the Case of University D

Establishment of Performance Evaluation System for Major Curriculum: Focusing on the Case of University D - major competency base;curriculum accreditation system;major curriculum operating system;educational satisfaction;major competency achievement
A Study on the Perception of School Members on Mid- and Long-Term Education Agenda at the Local Education Offices: Focused on Busan Metropolitan City Office of Education

A Study on the Perception of School Members on Mid- and Long-Term Education Agenda at the Local Education Offices: Focused on Busan Metropolitan City Office of Education - education policy;education agenda;publicity of education;future education
Exploring the Characteristics of Contemplative Intuition for the Expression of Self-transcendence Consciousness

Exploring the Characteristics of Contemplative Intuition for the Expression of Self-transcendence Consciousness - self-transcendence consciousness;contemplative intuition;Nhat Hanh;Merton;Palmer;educational activities
Intelligent Robot Design to Assistance the Elderly in Toilet

To assist the elderly people living alone to go to the toilet and ensure their safety, based on the Fischer component, an intelligent robot device is designed to assist the elderly in toilet use.The whole device has a simple structure, convenient control, diversified functions, simple operation, is friendly to the elderly, and can better meet the life and health needs of the elderly living alone.It also provides a direction for the robot's physical design.
Terrorism-Related Topic In The Language Of Fake News In Indonesia

he discussion on the relations between terrorism and social media mainly focuses on social media's role in spreading the terrorism ideology and recruiting the member of the terrorist group. However, social media has also become means to share fake news related to terrorism. Considering the relationship between fake news and terrorism, the primary purpose of this study is to examine the content, language used, and emotion of fake news about terrorism in Indonesia. It also reveal what kind of language used in fake news to manipulate the public and what emotions it appeals to. We analyzed fake news about terrorism in Indonesia using quantitative text analysis methods, such as sentiment analysis and dictionary-based analysis methods with R statistical software. We created two dictionaries covering the religious and violence words, and fake news was examined under these two dictionaries. For reliability, we applied the "Split-half test" to the results and we reached similar results. This study shows that ISIS, Aceh, and terrorist action in Sulawesi are three dominant topics in fake news. Second, the language on fake news is mostly about terrorism and violence to create fear in society. Third, violence and religious language are equal in the language of misleading content. We interpreted the results obtained with the traditional fear of crime theory. We then discussed the major results of this research and made effort to explain reasons behind our research results. The study has a limitation because of the small number of fake news. Future studies may overcome this limitation by selecting multiple countries as cases or increasing the date range of fake news. The dictionary-based method we used in this study is relatively new for the literature and provides the opportunity to analyze fake news content effectively. Our results using the Dictionary-based method can provide valuable clues for policymakers in the counter-terrorism field.
Multi-Source and Multi-Dimensional Data Fusion of Magnetic Levitation Track Transportation Based on Digital Twin

Digital twin (DT) of large-scale transportation infrastructure plays an important role in the development of intelligent transportation system (ITS), and has become the current research hotspot of ITS.Traditional data fusion has done a lot for intelligent transportation infrastructure.However, it still exists many shortcomings.This paper aims at establishing a multi-source and multidimensional data fusion model of magnetic levitation track based on digital twin.We proposed a data fusion method that can fuse 2D image data and 3D LIDAR point clouds data together, by using Context Capture and Cloud Compare software.This method combines data advantages so that we can optimize the expression of fine particle accuracy.Firstly, we made the aerial triangulation for the image data that was collected with drone, and then reconstructed the dense point clouds and generated the colorful point clouds; next, we fused the colorful point clouds with the LIDAR point clouds data that has been data processed; and finally, we generated the model and accomplished the fusion process of magnetic levitation track model.We compared the digital twin model with the benchmark model from macroscopic to microscopic perspective, the verification results indicated that the error of track flatness is about one centimeter, and the mean distance between the two models is about 0.124 meters, so the digital twin data fusion model fits well.
Comparison of two model environmental burdens with massive light non-aqueous phases liquids (LNALP) pollution – the extent of pollution identified by geological survey vs. reality uncovered by remediation

The paper deals with the results of exploration and remediation in two railway operations. Locomotive depots in Štúrovo and Leopoldov belonged to heavily polluted sites with the occurrence of light non-aqueous phases liquids (LNAPL) on the groundwater table. Remediation began on the sites in 2018. The results of the pre-remediation surveys did not indicate the presence of large volumes of pollution in the form of free phase, however, during the remediation LNAPL flowed into new wells. At the same time, the spreading of LNAPL accumulations and the progressing of pollution in dissolved form by groundwater has not been confirmed. Comparison of surveys and remediation points to various facts that must be considered when evaluating the degree of pollution of the site. It is necessary to perceive the whole space of the railyard as possible source of pollution. Remediation project should not be based solely on the presence of LNAPL. Its presence and the risk it poses must be verified. Outcomes of activities, based on limited data, may in that case differ from the remediation goals.
Development of a Basic Chemistry Conversational Corpus

Chatbot technology can be an important tool and supplement to education, leading to explorations in this area.Corpus-based chatbot building has a relatively low entry barrier as it only requires a relevant corpus to train a chatbot engine.The corpus is a set of human-readable questions and answers and may be an amalgamation of existing corpora.However, a suitable chemistry-based chatbot corpus catering for a freshman general chemistry course addressing inorganic and physical chemistry has not been developed.In this study, we present a basic chemistry conversational corpus consisting of 998 pairs of questions and answers, focused on a freshman general chemistry course addressing inorganic and physical chemistry.Ten human raters evaluated the responses of a chatbot trained on the corpus and suggests that the corpus resulted in better response than random (t = 17.4,p-value = 1.86E-53).However, only 20 of the 50 test questions show better responses compared to random (difference in mean score ≥ 1.9, paired t-test p-value ≤ 0.0324), suggesting that the corpus provides better responses to certain questions rather than overall better responses, with questions related to definitions and computational procedures answered more accurately.Hence, this provides a baseline for future corpora development.