Reviewerly/exHarmony huggingface dataset:
Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case–control study

Heart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates.GP data from 8543 patients (-2 to -1 year before diagnosis) and controls aged 70+ years were obtained retrospectively from 01 January 2012 to 31 December 2019 from the Nivel Primary Care Database. Codes about chronic illness, complaints, diagnostics and medication were obtained. Data were split in a train/test set. Datasets describing demographics, the presence of codes (non-sequential) and upon each other following codes (sequential) were created. Logistic regression, random forest and XGBoost models were trained. Predicted outcome was the presence of HF after 1 year. The ratio case:control in the test set matched true incidence rates (1:45).Sole demographics performed average (area under the curve (AUC) 0.692, CI 0.677 to 0.706). Adding non-sequential information combined with a logistic regression model performed best and significantly improved performance (AUC 0.772, CI 0.759 to 0.785, p<0.001). Further adding sequential information did not alter performance significantly (AUC 0.767, CI 0.754 to 0.780, p=0.07). The number needed to screen dropped from 14.11 to 5.99 false positives per true positive.This study created a model able to identify patients with pending HF a year before diagnosis.
Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care–Associated Infection

With the widespread adoption of electronic healthcare records (EHRs) by US hospitals, there is an opportunity to leverage this data for the development of predictive algorithms to improve clinical care. A key barrier in model development and implementation includes the external validation of model discrimination, which is rare and often results in worse performance. One reason why machine learning models are not externally generalizable is data heterogeneity. A potential solution to address the substantial data heterogeneity between health care systems is to use standard vocabularies to map EHR data elements. The advantage of these vocabularies is a hierarchical relationship between elements, which allows the aggregation of specific clinical features to more general grouped concepts.This study aimed to evaluate grouping EHR data using standard vocabularies to improve the transferability of machine learning models for the detection of postoperative health care-associated infections across institutions with different EHR systems.Patients who underwent surgery from the University of Utah Health and Intermountain Healthcare from July 2014 to August 2017 with complete follow-up data were included. The primary outcome was a health care-associated infection within 30 days of the procedure. EHR data from 0-30 days after the operation were mapped to standard vocabularies and grouped using the hierarchical relationships of the vocabularies. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and F1-score in internal and external validations. To evaluate model transferability, a difference-in-difference metric was defined as the difference in performance drop between internal and external validations for the baseline and grouped models.A total of 5775 patients from the University of Utah and 15,434 patients from Intermountain Healthcare were included. The prevalence of selected outcomes was from 4.9% (761/15,434) to 5% (291/5775) for surgical site infections, from 0.8% (44/5775) to 1.1% (171/15,434) for pneumonia, from 2.6% (400/15,434) to 3% (175/5775) for sepsis, and from 0.8% (125/15,434) to 0.9% (50/5775) for urinary tract infections. In all outcomes, the grouping of data using standard vocabularies resulted in a reduced drop in AUC and F1-score in external validation compared to baseline features (all P<.001, except urinary tract infection AUC: P=.002). The difference-in-difference metrics ranged from 0.005 to 0.248 for AUC and from 0.075 to 0.216 for F1-score.We demonstrated that grouping machine learning model features based on standard vocabularies improved model transferability between data sets across 2 institutions. Improving model transferability using standard vocabularies has the potential to improve the generalization of clinical prediction models across the health care system.
All that glitters...

Evaluation has emerged as a central concern in natural language processing (NLP) over the last few decades. Evaluation is done against a gold standard, a manually linguistically annotated dataset, which is assumed to provide the ground truth against which the accuracy of the NLP system can be assessed automatically. In this article, some methodological questions in connection with the creation of gold standard datasets are discussed, in particular (non-)expectations of linguistic expertise in annotators and the interannotator agreement measure standardly but unreflectedly used as a kind of quality index of NLP gold standards.
Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background :To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement.Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment).This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement.Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing.Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models.Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively.Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model.Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment.On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model.
Features of Entrepreneurship Training in Non-core Areas of Secondary Vocational Education Programs

The article discusses the issues of entrepreneurship training among students in various educational programs of secondary vocational education, such as Commerce (by industry), Hospitality, Design (by industry), Economics and Accounting, Preschool education, Internet marketing, Information systems and programming. The study of entrepreneurial disciplines, which are not specialized, is based on the formation of general professional competencies and can be based on the practical interests to students of various professions. In modern conditionscollege graduates can open their own business as individual entrepreneurs and self-employed citizens, creating their own business without forming a legal entity, as well as initiate intra-company entrepreneurshipThe article substantiates the additional opportunities that college graduates can get in the case of studying entrepreneurial disciplines along with obtaining the competencies necessary for the chosen profession. The authors propose to consider the process of teaching entrepreneurship in non-core areas of secondary vocational education programs by building a system of competencies based on the systemic addition of a set of professional competencies with competencies in the field of entrepreneurship. The authors present an example of the content of the academic discipline “Fundamentals of Entrepreneurship”, designed to form the entrepreneurial competencies of students in various fields at the college at the Moscow Financial and Industrial University “Synergy”. Also the authors propose the main topics, forms of education and types of training sessions necessary to achieve the required level of professional competence, taking into account the development of entrepreneurial competencies.
Typed properties and negative typed properties: Dealing with type observations and negative statements in the CIDOC CRM

A typical case of producing records within the domain of conservation of cultural heritage is considered. During condition and collection surveys in memory organisations, surveyors observe types of multiple components of an object but without creating a record for each one. They also observe the absence of components. Such observations are significant to researchers and are documented in registration forms but they are not easy to implement using popular ontologies, such as the CIDOC CRM which primarily consider individuals. In this paper techniques for expressing such observations within the context of the CIDOC CRM in both OWL and RDFS are explored. OWL cardinality restrictions are considered and new special properties deriving from the CIDOC CRM are proposed, namely ‘typed properties’ and ‘negative typed properties’ which allow stating the types of multiple individuals and the absence of individuals. The nature of these properties is then explored in relation to their correspondence to longer property paths, their hierarchical arrangement and relevance to thesauri. An example from bookbinding history is used alongside a demonstration of the proposed solution with a dataset from the library collection of the Saint Catherine Monastery in Sinai, Egypt.
The Dynamics of Corruption, Human Capital Development and Economic Performance in Nigeria

This study examined the relationship among corruption, human capital development and Nigeria's economic performance with the motivation to answer questions linked to empirical data reported in recent literature.The study deployed the Auto Regressive Distributed lag (ARDL) to test the short-run and long-run relationship among dependent and independent variables using annual time series data from the World Bank's Development Indicators and the Central Bank of Nigeria over the period 1982 to 2020.Stemming from the empirical results, the short-run estimates revealed that human capital development, gross fixed capital formation, and government expenditure exert a positive and statistically significant impact on Nigeria's economic performance.However, the long-run estimation results indicate that corruption, government expenditure and trade openness exert a negative impact on economic performance.In the nutshell, economic performance responds negatively to changes in the country's level of corruption overtime.Nevertheless, it reacts positively to the changes in human capital development in the long-run.This implies that, with the availability of appropriate policies and resources, human capital development has the potential to massively enhance Nigeria's economic performance in the long-run.The nature of linkage between corruption and economic performance through human capital development remains less defended in the economic and social literature.
Text Selection and Preferences of EFL Students While Reading on Smartphones

Reading on smartphones is now a common activity due to the variety of platforms that cater to all kinds of users. The number of texts and range of difficulty levels in the English language are significant for EFL students because they affect their language acquisition efforts. This qualitative case study investigates the types of texts that EFL students engage with on smartphones and the approaches they use to select specific texts to read. Students submitted screen recordings of their smartphone activities and justified their actions through interviews. Findings were thematically reported. The study revealed the kinds of texts students engaged with and how they were selected and evaluated. Students generally avoided texts perceived as challenging but attempted texts with topics that attracted them. These findings shed more light on text features that students seek and create awareness for content creators to produce digital content that is engaging and relevant for educational purposes.
Improving Student Learning Outcomes through Mobile Assessment: A Trend Analysis

The integration of technology in learning is one of the popular issues today which is an alternative to solve the problems experienced in the difficulty of achieving maximum learning goals for students. This study aims to examine the effectiveness of the application of mobile assessment in learning activities in elementary schools. This research is a quantitative study using descriptive type. The respondents of this study were elementary school students who were at the fifth-grade level. Data were collected through learning outcomes tests using mobile assessment technology. The data analysis technique used is trend analysis. Research findings indicate that there is a positive trend in student learning outcomes when using mobile assessments in learning activities. Based on the model fit, the findings show that the Quadratic Trend Model is the best model with the smallest measurement error.
A Preliminary Study on the Barriers in Listening Section of English Proficiency Test: Students’ Perspectives

&#x0D; Abstract&#x0D; This study intends to find out the problems or barriers faced by Indonesian students in studying for an English proficiency test (EPT), especially in Listening section. EPT becomes a standard English test and a compulsory test for students since it is used as one of the requirements for their graduation at the degree level, and also for a job application. Those requirements increase the demand of the test. However, the fact shows that the test takers of the EPT took the test for times since they cannot pass the targetted score. This fact led this study to investigate further about the barriers they face in EPT. The study also describes kinds of English Proficiency Test and the kind used in University of Muhammadiyah Gresik. The design of the study is descriptive qualitative with 40 students as the subject. The data will be collected from questionnaire and follow-up interview to answer the question and reveal the barriers they face during the test.&#x0D; &#x0D;
Blockchain-Assisted Distributed Fog Computing Control Flow Attestation

The control flow hijacking attack poses a serious threat to the integrity of the software. The attacker exploits the loophole to hijack the control flow of the running program to achieve the purpose of the attack. Remote control flow attestation is a method for embedded devices to ensure the integrity of the software. With the continuous development of Internet of Things (IoT) technology, embedded devices have exploded. None of the existing control flow attestation schemes can adapt to the real-time attestation requests of such massive embedded devices. This paper proposes a blockchain-assisted distributed fog computing control flow attestation scheme BDFCFA to deal with this scenario. The scheme uses a simplified control flow representation model, which can effectively represent the control flow of the program and reduce the runtime overhead of the prover in the attestation process. We use SGX technology to protect the integrity and confidentiality of verifier and prover data during the attestation process. Our proposed bidirectional control flow attestation protocol based on the elliptic curve can greatly protect the communication security between verifiers and provers without incurring excessive performance overhead and communication cost. We evaluate the performance of BDFCFA through the SNU real-time benchmark and demonstrate that BDFCFA has better performance. Finally, compared to the existing remote control flow attestation scheme, the results show that BDFCFA has the highest security.
Automatic Modulation Classification for MASK, MPSK, and MQAM Signals Based on Hierarchical Self-Organizing Map

Automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.
Pengaruh Media Pembelajaran Powtoon Terhadap Hasil Belajar Matematika pada Materi Perkalian Siswa Kelas II Sekolah Dasar

The purpose of this study was to find out whether the use of Powtoon learning media affects the outcome of mathematics learning in elementary school multiplication materials. This study used a quantitative experiment method with a pre-test-post-test non-equivalent control design. The sample for this study was 40 students divided into Class II-A as an experimental class with 20 students and Class II-B as a control class with 20 students. In processing the data, researcher tested the analytical requirements, which consisted of a normality test using the Liliefors test with normal distribution results, and a homogeneity test for pre-test and post-test data using the Fishers test with homogeneous data results. The result of the hypothesis test using the t-test is tcount&gt; ttable (30,098&gt; 2,0930). From the result, it can be concluded that there is an effect of Powtoon learning media on mathematics learning outcomes in elementary school multiplication materials.&#x0D; Tujuan dilakukannya penelitian ini ialah menganalisis apakah terdapat pengaruh penggunaan media pembalajaran Powtoon pada hasil belajar matematika khususnya untuk materi perkalian sekolah dasar. Penelitian ini menerapkan metode kuantitatif menggunakan desain Pretest-Posttest nonequivalent control grup. Kemudian, sampel yang digunakan ada 40 siswa yang terbagi menjadi kelas eksperimen yakni kelas II-A sejumlah 20 anak dan kelas kontrol (kelas II-B) sejumlah 20 anak. Data diolah dengan melakukan uji persyaratan analisis, yakni uji normalitas dengan uji liliefors dengan hasil berdistribusi normal dan uji homogenitas data pretest dan post tes menggunakan uji fisher dengan hasil data homogen. Hasil pengujian untuk hipotesis dengan menerapkan uji-t dengan hasil thitung &gt; ttabel (30,098 &gt; 2,0930). Mengacu pada hasil yang diperoleh, disimpulkan bahwa ditemukan pengaruh media pembelajar powtoon pada hasil belajar matematika untuk materi perkalian siswa sekolah dasar.&#x0D;
Detection and Recognition of Traffic Sign Boards using Random Forest Classifier

The traffic sign recognition system is a vital aspect of an intelligent transportation system since it provides information to drivers to help them drive more safely and effectively. This paper addresses some of these concerns, which will be accomplished in two steps. The first is the detection of traffic signs, which is divided into two stages. After a picture has been preprocessed to emphasize relevant information, signs are segmented based on color thresholding, shape-based detection. The second task is the recognition of traffic signs. There are two steps involved in this method. In this study, Histogram of Oriented Gradient is utilized as a feature extractor, and Random Forest Classifier is used in the recognition stage. The findings of the experiment show that utilizing Random Forest Classifier resulted in an accuracy score of 95.59 %, precision of 97.55 %, recall of 95.37% in the recognition process and 90.34 % accuracy in the detection process.
Images Segmentation Based on Cutting the Graph into Communities

This article considers the problem of image segmentation based on its representation as an undirected weighted graph. Image segmentation is equivalent to partitioning a graph into communities. The image segment corresponds to each community. The growing area algorithm search communities on the graph. The average edge weight in the community is a measure of the separation quality. The correlation radius determines the number of next nearest neighbors connected by edges. Edge weight is a function of the difference between color and geometric coordinates of pixels. The exponential law calculates the weights of an edge in a graph. The computer experiment determines the parameters of the algorithm.