Reviewerly/exHarmony huggingface dataset:
Numerical Comparison of the Replacement Process Approach with the Aggregation-Disaggregation Algorithm for Row-Continuous Markov Chains*

In a recent paper by Sumita and Rieders (1990), a new algorithm has been developed for computing the ergodic probability vector for large Markov chains. Decomposing the state space into M lumps, the algorithm generates a sequence of replacement processes on individual lumps in such a way that in the limit the ergodic probability vector for a replacement process on one lump will be proportional to the ergodic probability vector of the original Markov chain restricted to that lump. In a sequel to the original paper by the same authors, this idea is applied to row-continuous Markov chains, where the underlying skip-free structure enables one to construct the replacement distributions explicitly without involving any inverse matrices. This paper discusses the numerical study of the replacement process approach for such 288chains in comparison with Takahashi’s modified aggregation-disaggregation algorithm and a modification of Van der Heyden’s algorithm. When successive substitution is employed as a means to solve systems of linear equations involved in either algorithm, the extensive numerical experiments suggest that the replacement process approach is more efficient than Takahashi’s modified algorithm by a factor of three to five. Furthermore, the efficiency gap increases as the size of the Markov chain increases.
Compilation and Application of Microcomputer Physical Education Class Scoring System

Abstract Nowadays, computer technology has been applied to all walks of life. Therefore, colleges and universities have set up computer-related courses for students. This paper mainly introduces the functional information of the physical education scoring system.
Spatio Temporal Modelling for Government Policy the COVID-19 Pandemic in East Java

COVID-19 has cursorily spread globally. Just in four months, its status altered into a pandemic. In Indonesia, the virus epicenter is identified in Java. The first positive case was identified in West Java and later spread in all Java. The Large-scale Social Restrictions are seemingly inefficient as the SARS-CoV-2 transmission remains. As such, the government is struggling to find anticipatory policies and steps best to mitigate the transmission. In this particular article, we used a Spatio-temporal model method for the total COVID-19 cases in Java and forecasted the total cases for the next 14 days, allowing the stakeholders to make more effective policies. The data we were using were the daily data of the cumulative number of COVID-19 cases taken from www.covid19.go.id. Data modelling was conducted using a generalized spatio-temporal autoregressive model. The model acquired to model the COVID-19 cases in Java was the GSTAR(1)(1,0,0) model.
Constructivism in the training of students of pedagogical specialties

The purpose of this article is to evaluate the effectiveness of models of training students in pedagogical specialties based on the theory of constructivism. The research methodology is based on a survey of students on the acquisition of knowledge in the learning process on the example of universities in Ukraine and Poland. The results of the study demonstrate the formation of students' knowledge in the process of organizing learning based on the methods of pedagogical constructivism. Students of the experimental group demonstrate a higher level of activity if the teacher uses communication and trust-oriented teaching methods. The scientific value of this research lies in the possibility of using the results of student surveys by teachers of pedagogical universities to understand which elements of constructivist pedagogy should be used to increase the efficiency of the educational process.
New technologies and pedagogical profession

The purpose of the study is the analysis of best practices in the implementation of new technologies in the system of university education and their impact on the development of the teaching profession. Research tasks: - to conduct a sociological survey of teachers of higher educational institutions on the impact of new technologies on the teaching profession in order to identify problematic and positive aspects of the introduction of new educational technologies; - to analyze the best practices of introduction of new educational technologies in the leading universities of the world for the purpose of formation of the generalized algorithm of adaptation of new technologies to educational process and development of a pedagogical profession; - formulation of recommendations for ensuring high quality of online education and development of the pedagogical profession in the conditions of online learning. we note that new technologies will certainly improve the skills of teachers, but provided that the university creates the necessary working conditions.
Quantum algorithm for the calculation of transition amplitudes in hybrid tensor networks

The hybrid tensor network approach allows us to perform calculations on systems larger than the scale of a quantum computer. However, when calculating transition amplitudes, there is a problem that the number of terms to be measured increases exponentially with that of contracted operators. The problem is caused by the fact that the contracted operators are represented as non-Hermitian operators. In this study, we propose a method for the hybrid tensor network calculation that contracts non-Hermitian operators without the exponential increase of the number of terms. In the proposed method, calculations of transition amplitudes are performed by combining the singular value decomposition of the contracted non-Hermitian operators with the Hadamard test. The method significantly extends the applicability of the hybrid tensor network approach.
Enhancing a mobile application with contextual information delivery using proximity beacon: a preliminary study at a tourist destination

Since then, the utilization of cell phones has developed from simply messaging and calling to multimedia related capacities, video gaming, productivity tools, and travel guides.This present paper proposes a development of mobile application in order to enhance and attract tourists to visit the tourism destinations.In this study, the technologies used are geofence and proximity beacon which can be placed at any attractive points programmed to deliver information when tourists are in proximity ranges of the beacon transmission signal.In addition, the information will be automatically pushed and displayed in the application.This study was conducted at Melaka Zoo, Malaysia.This paper reviews related articles on contextual awareness, location-based services, proximity beacon, geofence, mobile cloud computing, and tourism industry in Section 2. Section 3 describes the methodology of this study.Findings and results of all tests conducted are presented and discussed in Section 4. Section 5 concludes this study. 2.
Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.

e13555 Background: Contra to national guidelines, women with ovarian cancer often receive aggressive treatment until the end-of-life. We trained machine learning algorithms to predict mortality within 180 days for women with ovarian cancer. Methods: Data were collected data from a single academic cancer institution in the United States. Women with recurrent ovarian cancer completed biopsychosocial patient-reported outcome measures (PROMs) every 90 days. We randomly partitioned our dataset into training and testing samples with a 2:1 ratio. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into a voting ensemble. We assessed the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) for each algorithm. Results: We recruited 245 patients who completed 1319 assessments. The final voting ensemble performed well across all performance metrics (Accuracy = .79, Sensitivity = .71, Specificity = .80, AUROC = .76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment Conclusions: Machine learning algorithms trained using PROM data offer state-of-the-art performance in predicting whether a woman with ovarian cancer will reach the end-of-life within 180 days. We highlight the importance of PROM data in ML models of mortality. Our model exhibits substantial improvements in prediction sensitivity compared to other similar models trained using electronic health record data alone. This model could inform clinical decision making and improve the uptake of appropriate end-of-life care. Further research is warranted to expand on these findings in a larger, more diverse sample.
Iot Based Covid-19 Patient Health Monitoring System

Medical Surveillance Solutions are the most important in the brief developing country populace enhances demands for caretaking. Covid-19 is as a substitute contagious it is very important to quarantine covid-19 humans but at the equal time medical examiners need to check fitness of covid-19 sufferers moreover. With the boosting kind of instances it's miles turning into tough to preserve a tune on the health and well-being problems of several quarantined humans. Below the encouraged machine design of a Wi-Fi sensor network based upon IoT innovation. It is normally used for accumulating as well as moving the unique sensors tracking data regarding the human beings in healthcare centers. This software consists of Wireless primarily based network (Wi-Fi), having absolutely exceptional sensing gadgets related to the transmitter area the ones are Heart beat sensing unit, Temperature stage sensing unit blood pressure sensor and pulse oximeter. These sensors are straight away connected to the affected man or woman and accumulate the client issues by using way of the use of sensing devices. The same statistics is sending out wirelessly to the receiver location this is with the medical agent and by way of that receiver trouble he'll definitely reap all updates in their clients. And additionally it's going to genuinely deliver voice word to humans to take there medicines suitable time. And one sharp buzzer will in fact there at patient with a view to virtually advocate emergency state of affairs of clients. When patient will press emergency button then the buzzer will be ON.
Pengaruh Pemahaman Siklus Akuntansi, Computer Attitude, Intensitas Latihan Soal dan E-Learning terhadap Hasil Belajar Komputer Akuntansi

The research objective was to prove the effect of understanding the accounting cycle, computer attitude, question practice intensity and e-learning simultaneously (together) or partially (alone) on the learning outcomes of accounting computer learning. This type of research is a quantitative research. The analysis technique used multiple linear regression test assisted by the SPSS 25 application program. The research sample consisted of 125 students from class XI AKL SMK Negeri 1 Pati, which was taken randomly based on the proportional random sampling method and using the calculation of the Slovin formula. Data collection techniques are using tests, questionnaires and documentation. The results of the study proved that the understanding of the accounting cycle, computer attitude, question training intensity and e-learning influenced the learning outcomes of class XI AKL SMK Negeri 1 Pati. Acquisition of t test results prove that each variable, namely the understanding of the accounting cycle, computer attitude, the intensity of question exercises and e-learning partially affect the learning outcomes of accounting computer. Based on the research results, it is hoped that further researchers can develop this research, by expanding the scope of the research sample and adding variables that are thought to affect the learning outcomes of accounting computers such as learning styles, learning discipline, English language skills and computer mastery.
Computer Vision-Based Framework for Anomaly Detection

Computer vision involves analysis and processing of images. The feature that makes computer vision a choice for research is the use of high dimensional data for generating image information. Object tracking and trajectory modelling are the two main vital applications of computer vision. In today’s scenario, million and billions of cameras are installed only in India, which is a potential source of data that can be used for extracting useful information. While tracking the movement of objects in a crowded scenario, it is of utmost importance to efficiently detect groups. By doing so one can easily maintain the crowd interaction and at the same time can detect the anomalies. In this paper, various relevant methods for anomaly detection have been reviewed. After studying those methodologies a framework for small group detection into a crowded scene has also been proposed in which with the help of morphological operations the groups are decided. The implementation of the proposed framework is to be seen as ongoing research work.
Improving the Performance of UDify with Linguistic Typology Knowledge

UDify is the state-of-the-art language-agnostic dependency parser which is trained on a polyglot corpus of 75 languages.This multilingual modeling enables the model to generalize over unknown/lesser-known languages, thus leading to improved performance on low-resource languages.In this work we used linguistic typology knowledge available in URIEL database, to improve the cross-lingual transferring ability of UDify even further.
COVID-19: The Open Data Pandemic

The Coronavirus disease (COVID-19) pandemic highlighted the need for trustworthy data and science to guide the response of authorities, institutions and the public. Data in isolation lacks purpose or value and may pose little danger to its custodian from privacy or from the ability to manipulate it. The urgent and pressing need for a cure is forcing researchers and scientists to put the benefit in front of the legalities: sharing models, research and analytics accelerating science ahead of regulation. In the number of months since the pandemic recorded its first case, open science has led to the isolation of the virus, a standardized list of symptoms, COVID-19's genome has been sequenced and shared, multiple formats of testing have evolved and the potential of global vaccines have been developed across multiple jurisdictions. Prior to the pandemic, science was largely publicly funded, but not publicly accessible.
Analysing Large Inconsistent Knowledge Graphs Using Anti-patterns

A number of Knowledge Graphs (KGs) on the Web of Data contain contradicting statements, and therefore are logically inconsistent. This makes reasoning limited and the knowledge formally useless. Understanding how these contradictions are formed, how often they occur, and how they vary between different KGs is essential for fixing such contradictions, or developing better tools that handle inconsistent KGs. Methods exist to explain a single contradiction, by finding the minimal set of axioms sufficient to produce it, a process known as justification retrieval. In large KGs, these justifications can be frequent and might redundantly refer to the same type of modelling mistake. Furthermore, these justifications are –by definition– domain dependent, and hence difficult to interpret or compare. This paper uses the notion of anti-pattern for generalising these justifications, and presents an approach for detecting almost all anti-patterns from any inconsistent KG. Experiments on KGs of over 28 billion triples show the scalability of this approach, and the benefits of anti-patterns for analysing and comparing logical errors between different KGs.
Models For Training Teachers For Work In A Heterogeneous Educational Environment

The article discusses the ways of training teachers for work in a heterogeneous educational environment.In a situation of cultural, ethnic, religious, and social diversity expansion in society, it is necessary to design various models for training teachers to work with heterogeneous groups and organizations.The authors analyze the most common models of teacher training based on the generalization of Russian and international experience.The criteria of analysis include comprehensive training of teachers for professional activity in the conditions of diversity; availability of Master degree programs; degree of these programs' continuity with the Bachelor degree programs; representation of diversity in international projects.Based on these criteria, the authors identify continuous (longitudinal), single-level, fragmentary, discrete and situation-consulting models of training teachers to work with heterogeneous groups and organizations.The article considers the continuous model as the most optimal one which provides a wide range of competencies necessary for a teacher to work in a heterogeneous environment.This model includes best practices that are reflected in international projects of the "Erasmus+" program.The advantage of the continuous model is in ensuring the continuity of educational programs implemented at various levels (Bachelor's, Master's, and professional development) and gradual expansion of the range of knowledge and skills that are in demand in a heterogeneous society.The implementation of the continuous model in the university practice is illustrated by the cases of implementing Master degree programs for training teachers and educational managers at Russian and foreign universities.
Article Desktop Based National Police Commission Activities Information System

In the implementation of the activities of the National Police Commission (Kompolnas) every month it is necessary to prepare a monthly report by the Secretariat of the National Police Commission to be reported to the Chair of the National Police, so that it can be used as a benchmark for the implementation of Kompolnas performance. However, making monthly reports is often late and mistaken because there are factors that influence, including: data storage such as official travel administration and irregular activity schedules making it difficult to find data, difficult to monitor ongoing activities, many activities implemented and others. In this regard, a desktop-based information system for the implementation of Kompolnas is required. The research method uses Research and Development to create a desktop-based Kompolnas activity information system. Meanwhile, the data teaching technique used literature study and field study (interview and document examination). Then the system development method used is the waterfall method which has several stages, namely: needs analysis, system design, coding &amp; testing, program implementation, and maintenance. The research resulted in a supporting information system for compiling a monthly report on Kompolnas activities because data on Kompolnas activities such as reference documents, orders, materials, photos / videos, are easy to input and can be viewed, downloaded and printed. In doing so, it can realize a paperless office, thus giving the printer paper, ink and time.
A Bayesian active-learning test for remote hearing-aid fitting

A major problem of remote or online hearing tests is the calibration of the equipment and control of the acoustic environment: For example, an audiogram requires calibrated headphones with a wide dynamic range and a silent room. Instead, a notched-noise test alleviates these problems and gives valuable information about auditory filters that can be used to fit a hearing aid. Although absolute levels still play a role, relative levels between signal and noise are more robust as the environment does not need to be quieter than the noise. Using traditional methods, determination of auditory-filter shapes across the whole frequency range would take hours. We developed a notched-noise test that uses Gaussian Processes to maintain a probabilistic estimate of the outcome, and to select stimulus parameters based on mutual information. This active-learning method gives an estimate of auditory-filter shapes in about 30 minutes across a wide range of frequency. We demonstrate that these estimates, even when obtained with headphones for which the frequency response and exact calibration is unknown, are sufficient to give a good initial fit of a hearing aid.
FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models

Variational autoencoders (VAEs) are widely used for latent variable modeling of text.We focus on variations that learn expressive prior distributions over the latent variable.We find that existing training strategies are not effective for learning rich priors, so we add the importance-sampled log marginal likelihood as a second term to the standard VAE objective to help when learning the prior.Doing so improves results for all priors evaluated, including a novel choice for sentence VAEs based on normalizing flows (NF).Priors parameterized with NF are no longer constrained to a specific distribution family, allowing a more flexible way to encode the data distribution.Our model, which we call FlowPrior, shows a substantial improvement in language modeling tasks compared to strong baselines.We demonstrate that FlowPrior learns an expressive prior with analysis and several forms of evaluation involving generation.
Bullying in individuals with cleft lip and palate: A Twitter analysis

This study aimed to evaluate bullying in individuals with cleft lip and/or palate (CLP) using Twitter analysis.By using Twitter's advanced search feature, the four selected keywords; "bully", "bullying", "bullied" and "teased" were searched along with the terms "cleft lip and palate", "cleft lip" and "cleft palate", respectively. A total of 1010 tweets meeting the criteria were selected. The data were evaluated qualitatively using thematic analysis and the determination of the themes were based on this analysis.Most of the tweets were posted by a layperson (80.8%), while most of the tweets were categorised in the theme "criticism of bullying" (36%). The tweets were distributed according to their uploader status as irrelevant individuals (60.6%), CLP subjects (26.9%), family of CLP subjects' (12.5%). Of the 1010 tweets evaluated, 707 were identified as negative, while 90 were identified as neutral and 213 were identified as positive. No significant differences were detected in retweet and like numbers between negative, neutral and positive comments (P > .05).Twitter is an important platform, in which the bullying victims with CLP and their families feel free to share their personal experiences of being bullied and how they feel about it, while the others are standing against bullying and promoting social support for these individuals.