Reviewerly/exHarmony huggingface dataset:
ClustEHR: a tool for generating synthetic EHR data for unsupervised learning experiments.

ObjectivesClustering algorithms are commonly used to identify disease clusters. New Clustering algorithms are benchmarked on synthetic data to assess their accuracy. These datasets lack the complexities of real electronic health record (EHR) producing a partial assessment of the algorithm. We developed a synthetic EHR cluster generator for benchmarking clustering algorithms.&#x0D; ApproachWe have created a synthetic EHR cluster generator, clustEHR, based on Synthea, a synthetic EHR generator that produces datasets (with parameterized noise and cluster separation) of known clusters and with clinically relevant patient outcomes. We evaluated clustEHR by generating multiple datasets of variable cluster separation and percentage of noise variables to reflect easy and hard clustering problems. We used a linear model to assess the relationship between these parameters and cluster problem difficulty. K-means accuracy was used as a proxy to measure cluster problem difficulty.&#x0D; ResultsWe have developed a tool for generating synthetic EHR cluster data with clinically relevant outcomes based on the rate of decline of medical observations (e.g. blood pressure). The following parameters are supported: a) number of clusters, b) number of patients in each cluster, c) number and data type of features, d) separation through defining clusters as either diseases such as COPD or dementia (high separability) or inter-disease conditions such as emphysema and chronic bronchitis within COPD (low separability), and e) noise variables through identifying variables not predictive of true cluster outcomes random forest feature importance metric. We show that high cluster separation significantly increases k-means accuracy (coefficient of 0.33). Smaller percent of noise variables increase accuracy though not significantly (coefficient 0.42).&#x0D; ConclusionClustEHR offers realistic mixed data types as well as outcomes which are frequently used to evaluate clusters when subtyping diseases. The evaluating results suggest that the difficulty of the cluster data can be user determined. The tool can be used to create realistic datasets for evaluating clustering approaches.
A Study on the Analysis of Learning Gap Based on Mathematical Performance Data in Middle School

This study is to explore the patterns of learning gap in math and provide a suggestion to resolve the problem for middle school students. The research adopted the view that the learning gap caused by differences in student individual competencies and family environments affects later learning, which can further deepen the learning gap. To do so, middle school grade disclosure data across the country from 2016 to 2020 were analyzed to figure out increases or decreases in standard deviation of students
LEGAL REGULATION AND CHARACTERISTICS OF ELECTRONIC SUMMONS UNDER APC

Digital transformation in the administration of justice is a complex and long-term process. Notifying the parties and exercising procedural rights electronically is a key point in the administrative process. The present study examines the specifics of the electronic subpoena of lawyers in administrative proceedings that take place before the court. Notifying and summoning the subjects ensures that the parties in the proceedings are informed about the upcoming or already performed actions in the case. The use of technology and electronic communication are part of the process of digital transformation of the judicial system and introduction of electronic justice.
Integrated planning of UAV 3D trajectory based on ant colony algorithm

Abstract To address the problem of UAV trajectory planning optimality, a dynamic multi-objective optimal path model is established based on satisfying the basic conditions of UAV flight constraints, taking into account the time window restricted area and radio interference threat, etc. comprehensively. Based on the characteristics of the complex environment, an ant colony algorithm with reduced adaptive step size is constructed based on the theory of ant colony algorithm to quickly plan the best safe UAV trajectory in the complex dynamic environment. Simulation results show that the model can meet the complex airspace conditions of UAV missions, while the algorithm can find shorter trajectories and shorten the search time.
The Effect of Learning Strategy and Locus of Control on Learning Outcomes by Controlling Early Knowledge

The purpose of this study is to empirically study the phenomena that occur related to learning strategies and locus of control by considering the variables of students' prior knowledge of learning outcomes. This research was conducted at SMA Negeri 1 Air Putih, North Sumatra during August to October 2016, and used experimental research methods. The number of subjects in this study each consisted of 40 students consisting of two treatment classes. While the data analysis technique used ANKOVA at a significance level of 0.05. The results obtained: (1) Indonesian language learning outcomes between groups of students taught with CTL learning strategies are higher than groups of students taught using expository strategies after controlling for initial knowledge, (2) Indonesian language learning outcomes among groups of students who have locus of internal control is higher than the group of students who have external locus of control after controlling for initial knowledge, (3) the effect of interaction between learning strategies and locus of control on Indonesian language learning outcomes after controlling for initial knowledge.&#x0D;
A New Hybrid Method for Text Feature Selection Through Combination of Relative Discrimination Criterion and Ant Colony Optimization

Text categorization plays a significant role in many information management tasks. Due to the increasing volume of documents on the Internet, automated text categorization has been more considered for classifying documents in pre-defined categories. A major problem of text categorization is the high dimensionality of feature space. Most of the features are irrelevant and redundant impacting the classifier performance. Hence, feature selection is used to reduce the high dimensionality of feature space and increase classification efficiency. In this paper, we proposed a hybrid two-stage method for text feature selection based on Relative Discrimination Criterion (RDC) and Ant Colony Optimization (ACO). To this end, we applied RDC method, at first, in order to rank features based on their values. Features, then, which their values are lower than a threshold are removed from the feature set. In the second stage, as a wrapper method, an ACO-based feature selection method is applied, to select redundant or irrelevant features that have not been removed in the first stage. Finally, to assess the proposed methods, we have conducted several experiments on different datasets to indicate the superiority of our proposed algorithm. We aim to propose a hybrid approach which is computationally more efficient in much the same way as it is more accurate compared to the other embedded or wrapper methods. The obtained results endorse that the proposed method is of remarkable performance in text feature selection.
Sentimental Analysis with Web Engineering and Web Mining

Across the globe, people are using mobile devices due to the swift evolution of Internet facilities that opened the doors for the utilization of many online platforms mainly for social connectivity among the people and for sharing their thoughts, activities, and ideas on worldwide issues. A lot of consumers got habituated to looking for reviews and ratings of a product on related well-known websites before going to buy that product. It turned out to be a common norm to have an analysis on buying a product from the perspective of believability and security. If we can get huge, data related to products and therefore by analyzing the obtained information, we need to recognize the necessary details of the products. In such a scenario, sentimental analysis acts handy for 198recognizing or understanding the thoughts and reactions of consumers. Sometimes, sentiment analysis is also considered thought exploration or thought mining. SVD (singular value decomposition) and PCA (principal component analysis) are utilized in this chapter for the identification of sentiments or thoughts from text mining to improve accuracy and decrease the duration of implementation. The major contributions made in this analysis are as follows: the first one for the preprocessing of the information collected by using a proposed algorithm for creating a proper basis for classification of sentiments, the second is the supplementary attributes to improve the accuracy of the classification, the third is applying SVD and PCA on the obtained data, and the last one is to identify and associate the results of the performance by designing five segments based on various attributes with or without lemma. The test results demonstrate that the current approach is more reliable than other approaches and that its duration of implementation can be minimized by its current approach.
Personalized and adaptive text recommendation for learners of Chinese

A text recommendation system helps language learners find suitable reading materials. Similar to graded readers, most systems assign difficulty levels or school grades to the documents in their database, and then identify the documents that best match the language proficiency of the learner. This graded approach has two main limitations. First, the common grade scale assumes that all learners find the same words difficult, ignoring different orders in vocabulary acquisition. Second, it cannot cater to the incremental vocabulary expansion typical of language learners, since discrete grades do not facilitate fine-grained adaptation in text recommendation. This article evaluates a personalized and adaptive method for text recommendation that aims to mitigate these limitations. This method recommends documents according to the new-word density preferred by the user. It estimates a document's new-word density with an open learner model that predicts the user's vocabulary knowledge. Users can dynamically update the learner model as they acquire new vocabulary. We conducted a user study involving ten learners of Chinese as a foreign language. Results shows that this method can estimate the learner model at 77.6% accuracy, outperforming the graded approach. Further, a user simulation demonstrates that it can recommend documents within 0.22% of the target new-word density, satisfying user preferences more accurately than the graded approach.
Automatic Digitization of Engineering Diagrams using Intelligent Algorithms

At present usage of computational intelligence became the ultimate need of the heavy engineering industries.Digitization can be achieved in these sectors by scanning the hard copy images.When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem.In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data.This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method.Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation.Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other.By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as misspelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.
Simplified Independent Model for Predicting Global Solar Radiation

In this investigation, five existing independent empirical models were calibrated and evaluated to calculate daily and monthly mean global solar radiation on a horizontal surface in Adrar city located in the south of Algeria, using meteorological data measured from 2013 to 2018. The measured data were divided into two periods; the first period (2015-2018) was used to calculate the empirical coefficients of the models, while the second period (2013-2014) was used to validate the correlations. Additionally, the best model (Al-Salaymeh model) was compared with five dependent empirical extreme air temperature models. In general, the results show that dependent models exhibited privileged performance than independent models. However, Al-Salaymeh regression independent model can contend with regression dependent models. Because of they use only the day number as a key input with smaller relative errors. It is found that daily statistical tests mean absolute bias error, root mean square error and coefficient of determination were equal to 2.0117 MJ/m&amp;sup2;, 2.4612 MJ/m&amp;sup2; and 0.8014 respectively. The best independent empirical model was also compared with the modified Algerian solar atlas model to show their effectiveness. As a conclusion, the simple independent models can satisfactorily describe the horizontal global solar radiation for Adrar.
Image Point Set Registration: Phased Gaussian Mixture Model

Abstract The introduction of probability model into point set matching has achieved great success. Because the image point set has more outliers and the image has complex transformation, the traditional Gaussian Mixture Model (GMM) does not perform well in image point set matching. In this paper, we propose a Phased Gaussian Mixture Model (PGMM) for point set registration between images. Different from the existing registration algorithms based on GMM, PGMM uses Locality Sensitive Hashing to distinguish the feature reliability of feature points. The initial transformation relation is obtained in the segment with high feature reliability and vector constraint is applied to other segments. The average Hamming distance is used to give different weights to each Gaussian component, which is related to the characteristics between data points and model points. The proposed method has good performance in the case of large image transformation scale.
Introduction

This chapter introduces the goals of the book Digital Humanism: A Philosophy for 21st Century Digital Society. It outlines the book’s chapter structure and discusses societal development as the context that requires Radical Digital Humanism today. The chapter argues that we need to ask the following questions: Why is Humanist philosophy important in the contemporary digital age? How can Humanism help us to critically understand how digital technologies shape society and humanity? What kind of Humanism do we need to make sense of digitalisation in society? The book Digital Humanism: A Philosophy for 21st Century Digital Society contributes to the renewal of Humanist philosophy in the digital age.
How and What has Influenced Cryptocurrency Prices? Economic influences or political Influences

Cryptocurrency has been prevailing gradually with repeating fluctuation in value. Cryptocurrency is not classified as a legal currency but a private financial management system that self-managed and is not managed by central banks or governments. Cryptocurrency is different from any other currency used around the world. Unlike central bank- and government-issued currency, the value of cryptocurrency is able to be inflated as the volume of it has a finite limit. The usage of cryptocurrency enables us to send or receive any amount of currency to anyone at a very cheap cost compared to other monetary instruments. Since the introduction of cryptocurrency, it has received much attention. The trading volume of cryptocurrency has been increasing rapidly although the value has crashed occasionally. Some serious incidents have occurred, however, cryptocurrency may have overcome these situations. How and what has influenced cryptocurrency value has been discussed a lot from the view of economic aspects, however, a common consensus has not yet been reached. For this issue, there is a large difference from stocks or other financial assets or financial instruments. However, there is some possibility that political influences rather than economic influences may have impacts on the prices of cryptocurrency. This study employs U.S.-China trade disputes and examines the effects on prices of cryptocurrency. The results show that political issues along with economic issues, including some asset prices, have some influence on cryptocurrency.
Investigating Cognitive Workload during Comprehension and Application Tasks in Software Testing

Software testers are an integral part of software development teams, and consequently need to understand from different perspectives the project entrusted to them.While developers might be required to understand a particular module or area of specialisation within a project, testers' comprehension requirements are more far-reaching [1].Gaining insights into how testers fare in different comprehension tasks is useful because it sheds light on how we could potentially support the efforts of the testing community.This paper reports the results of a laboratory experiment involving 15 professional software testers.Using NASA Task Load Index as our instrument of choice, we asked participants to carry out eight comprehension and application tasks across four categories (test case design, test automation, bug finding and adequacy analysis).We then analysed the data collected to seek to understand the effect of different task types, education level and participant experience on effectiveness and cognitive workload.The results suggest that, while experience is a key element in successful task completion, this is also influenced by task type.In fact, the more experienced persons actually tended to fare worse than their less experienced counterparts in certain tasks (namely, test case design and adequacy analysis).Level of education had no significant bearing on successful task completion but differences in cognitive workload could be observed for both experience and education-level variables.
Auto-encoder-based technique for effective detection of frauds in social networks

Detection of these spam accounts has recently attracted significant attraction in the literature. Most of the spam-account detection techniques presented in the literature employ supervised learning models to achieve their goal. These models require sufficient size of spam-account samples in their training set to be trained effectively. However, obtaining such large sample sizes is a significant challenge. In many real-world scenarios, the number of such available samples is extremely limited. Due to this limitation in the training set, the spam-account detection techniques can exhibit extremely poor detection accuracy. Hence, in this paper, an effective supervised learning model-based spam-account detection technique is presented, which utilises only limited size of spam-account samples in its training set, and to achieve this desired goal, the dimension of the feature vectors in the training set is reduced through the aid of auto-encoders. Further, the spam-accounts are detected based on their corresponding hazard rates. The hazard rates are generated through recurrent neural network. An empirical analysis study is presented, in which, the proposed spam-account detection technique is compared against the contemporary technique. In this study, the proposed technique exhibits relatively superior performance in-terms of classification accuracy.
A feature selection method based on neighbourhood rough set and genetic algorithm for intrusion detection

In intrusion detection system, unsupervised clustering algorithm is often used to analyse the detected data without class labels, and judge them as the normal or abnormal behaviour. Optimal feature subset can cut down the computational time of the clustering algorithm and effectively improve the intelligibility and accuracy of the clustering result. Therefore, this paper put forward a feature selection algorithm based on neighbourhood rough set and genetic algorithm. Firstly, neighbourhood rough set model, expanding the equivalence relation of discrete space to that of continuous space, was improved from class average distance of decision attributes and attribute significance two aspects. Then, genetic algorithm was used to select optimal feature subset based on improved attribute significance. Finally, in order to verify the feasibility, experiments were done on KDD CUP 99, and the results showed that the feature subset selected by the proposed algorithm ensured FCM getting higher accuracy.
Using Fault Topology to Reduce the Uncertainty During Fault Mapping

Summary Discovering interpretational errors while undertaking fault seal analysis is a common experience for practitioners. Integrating quality control checks for identifying technical issues early on, help to understand and reduce uncertainty. From a commercial perspective, this can same time and help to keep projects on schedule. It is particularly important in areas where data quality or data coverage may be an issue. In this contribution, we present case studies that give insights into the potential for using fault topology for the evaluation of structural maps.
Self regulation and discipline development to improve independence students in English course

The development of self-regulation and discipline can affect the way children learn independently. The independence of children's learning allows them to become individuals who are able to complete the independent way of the child's own personality. The purpose of this study is to see self-regulation of English subjects at the junior high school level. This study uses a quantitative approach with the type of ex post facto research. which was carried out at SMPN 2 Bengkulu City with a sample of 30 students specifically for English lessons. Data collection was done by using numbers and analyzed through regression analysis. The results of this study indicate that self-regulation partially or not affect students' learning independence; discipline towards student learning independence; as well as self-regulation and discipline towards student learning independence, especially in English subjects. This paper provides teachers to be more creative and implement student-centered learning. Optimizing discipline by providing fair and collective rewards.