Reviewerly/exHarmony huggingface dataset:
The comparison of selected machine learning techniques and correlation matrix in ICU mortality risk prediction

Identifying and analyzing mortality risk factors will lead to more accurate planning and prevention in health platforms. This research provides models for predicting mortality in the intensive care unit with machine-learning techniques. We extracted data from 1400 patients' medical records admitted to the intensive care unit of Imam Hossein Hospital in Tehran. Period of inclusion was January 1, 2018, to February 31, 2020. In this study, we used twelve predictor algorithms for data analysis, including: SVM, k-nearest neighbor, decision tree, logistic regression, random forest, MLP, RBF, Gradient Boosting, Fast Large Margin, Rule Model, Naive Bayesian, and Deep learning. We reported the performance of algorithms based on the precision, accuracy, sensitivity, specificity, and AUC. The findings showed that algorithms according to the percentage of AUC were 0.80 in Gradient Boosting, 0.781 in deep learning, 0.746 in logistic regression, 0.740 in SVM and 0.735 in MLP. Algorithm with the highest level of AUC was Gradient Boosting. Gradient Boosting's AUC = (0.8) was better at predicting mortality in intensive care units. 1) bilirubin 2) INR 3) Age 4) WBC had the strongest relationship with mortality in Gradient Boosting. Conclusion: Data analysis in ICU patients can be a valuable tool for predicting mortality and morbidity associated with mortality, but the results vary according to the data quality. However, the processes and methods mentioned in this study suggested that the rules extracted from the Gradient Boosting can be used as a model to predict mortality in intensive care units.
Systematic Review of Machine Learning-Based Open-Source Software Maintenance Effort Estimation

Background: Software maintenance is known as a laborious activity in the software lifecycle and is often considered more expensive than other activities. Open-Source Software (OSS) has gained considerable acceptance in the industry recently, and the Maintenance Effort Estimation (MEE) of such software has emerged as an important research topic. In this context, researchers have conducted a number of open-source software maintenance effort estimation (OMEE) studies based on statistical as well as machine learning techniques for better estimation. Objective: The objective of this study is to perform a systematic literature review (SLR) to analyze and summarize the empirical evidence of O-MEE ML techniques in current research through a set of five Research Questions (RQs) related to several criteria (e.g. data pre-processing tasks, data mining tasks, tuning parameter methods, accuracy criteria and statistical tests, as well as ML techniques reported in the literature that outperformed). Method: We performed a systematic literature review of 36 primary empirical studies published from 2000 to June 2020, selected based on an automated search of six digital databases. Results: The findings show that Bayesian networks, decision tree, support vector machines and instance-based reasoning were the ML techniques most used; few studies opted for ensemble or hybrid techniques. Researchers have paid less attention to O-MEE data pre-processing in terms of feature selection, methods that handle missing values and imbalanced datasets, and tuning parameters of ML techniques. Classification data mining is the task most addressed using different accuracy criteria such as Precision, Recall, and Accuracy, as well as Wilcoxon and Mann-Whitney statistical tests. Conclusion: This SLR identifies a number of gaps in the current research and suggests areas for further investigation. For instance, since OSS includes different data source formats, researchers should pay more attention to data pre-processing and develop new models using ensemble techniques since they have proved to perform better.
Query Evaluation by Circuits

In addition to its theoretical interest, computing with circuits has found applications in many other areas such as secure multi-party computation and outsourced query processing. Yet, the exact circuit complexity of query evaluation had remained an unexplored topic. In this paper, we present circuit constructions for conjunctive queries under degree constraints. These circuits have polylogarithmic depth and their sizes match the polymatroid bound up to polylogarithmic factors. We also propose a definition of output-sensitive circuit families and obtain such circuits with sizes matching their RAM counterparts.
Mobile Application in Teaching and Learning Process

The objective of this proposal is to investigate the effectiveness of mobile learning applications in Malaysia higher education. Technology and trends are growing rapidly every day and the evolution of 5G system is being implemented. So many different fields are now beginning to transform into technological elements education aspect also need to improve by add in technology to help more people can learn. The research aims are to develop an education application to help students and lecture more easily to learn and teach. This research will collect the data from different students by using structured questionnaires by using online tools. This study will conduct survey to collect and general the point of view of students towards the influence using mobile application to study and teaching. The interview will be focus on student study in high education. One of the contributions of mobile education application is let more people get education but in a cheaper way because some of the students can’t afford to buy heavy books or support expensive study fees education apps can help those students. In the future mobile education application can make improvement by adding in AI technology or IOT to make it more users friendly and able to help more students to study.
A DevSecOps-enabled Framework for Risk Management of Critical Infrastructures

This paper presents a Ph.D. research plan that focuses on solving the existing problems in risk management of critical infrastructures, by means of a novel DevSecOps-enabled framework. Critical infrastructures are complex physical and cyber-based systems that form the lifeline of a modern society, and their reliable and secure operation is of paramount importance to national security and economic vitality. Therefore, this paper proposes DevSecOps technology for managing risk throughout the entire development life cycle of such systems.
Steganography Method in Error-Correcting Codes

This article proposes a method of steganography based on the use of error-correcting codes, in which information is concealed by masking embedded information under bit errors using a secret key. The results of experimental estimates of system characteristics depending on operating modes of the system based on the use of the error control BCH code are presented. It is shown that changing parameters of codes provides flexible system configuration for different tasks. The system is ideal for passive attacks. Active and malicious attackers are able to interrupt the transmission process, but not to prove the presence of hidden information. The method allows organizing a covert information transmission channel regardless of the type of transmitted data.
An Efficient Ensemble-Based Deep Learning Model for the diagnosis of Cervical Cancer

Biomedical imaging has shown us the blueprints of the interior parts of the human body for clinical research and medical support. Cervical Cancer (CC) is a common disease found mostly in females. Pap smear, also known as pap test, is largely adopted to diagnose cervical cancer. Early diagnosis of cervical cancer and proper treatment can help us to save millions of lives. In this piece of work, we have designed a unique algorithm to split the data uniformly and further proposed an ensemble based approach to diagnose cervical cancer. Fusion of multiple deep learning models helped us to achieve the same and boost up the efficiency of the proposed approach. Results revealed that the proposed algorithm shows 97% accuracy in performing classification activity, identifying cancerous and non-cancerous cells.
The role of linearity in sharing analysis

Abstract Sharing analysis is used to statically discover data structures which may overlap in object-oriented programs. Using the abstract interpretation framework, we show that sharing analysis greatly benefits from linearity information. A variable is linear in a program state when different field paths starting from it always reach different objects. We propose a graph-based abstract domain which can represent aliasing, linearity, and sharing information and define all the necessary abstract operators for the analysis of a Java-like language.
Digitally Engineered Attention and Energy Theft via Psychological Manipulation

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Life and evolution</b> , and therefore humans, are dependent on energy. Human evolution and endeavor strive to control more concentrated and versatile forms of energy and to convert them at lower cost and with higher efficiency into heat, light, and motion <xref ref-type="bibr" rid="ref16" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[16]</xref> .
Effects of text messaging using digital Pinyin input on literacy skills of elementary school Chinese immersion learners

Children learning Chinese must cope with an opaque orthography lacking transparent relations between oral pronunciations and written characters: a challenge heightened for L2 learners. Use of digital Pinyin input may facilitate connections between oral and written language by allowing learners to access vocabulary they cannot yet write. We assessed the effects of promoting digital Pinyin writing by engaging students in text messaging in digital chat rooms as part of the 4th grade Chinese language arts curriculum at an American Chinese immersion school. Students in two classes engaged in text messaging over eight weeks while a matched group of students in other classes taught by the same teachers ( N = 28 per condition) completed their regular pencil-and-paper word work, which emphasized morphological and orthographic analysis of meaning- and sound-based radicals and sets of related characters. Post-intervention, children who engaged in text messaging showed lesser gains in Chinese writing as compared to the children who completed word work, though within texting groups children who did well at texting showed greater gains in writing as compared to children with lesser success at texting. Given the urgent need for effective online learning, the findings indicate that care should be taken when introducing digital Pinyin input into the Chinese language arts curriculum as it should supplement, but not replace, multi-component word work exercises that promote awareness of orthographic patterns, meaning, and Pinyin.
Two-Stage Algorithm for Segmentation of Satellite Images

A two-stage algorithm for segmentation of satellite images is proposed, which makes it possible to detect extended textural areas and small-sized objects. The method is based on the representation of multi-digit digital images by a set of bit images and the use of the mathematical apparatus of two-dimensional Markov chains. Transition probabilities for a two-dimensional Markov chain and brightness characteristics are used as textural features of extended objects. The selection of small-sized objects is performed on the basis of an estimate of the amount of information using a mathematical model of a two-dimensional Markov chain. To reduce computational resources, the evaluation of features is carried out using binary images of the highest, most informative, image digits. The average accuracy of segmentation of extended areas according to the F-measure metric is 68.9%, for small-sized objects is 47.5%.
Behavior-based test smells refactoring : Toward an automatic approach to refactoring Eager Test and Lazy Test smells

Software testing is an essential part of the development process, and like many software artifacts, tests are affected by smells, harming comprehension and maintainability. Several studies are related to test smell identification, but few studies are related to refactoring. Most proposed approaches are semi-automated, with the developer as a safety net. This paper presents a proposal for automatic refactoring of Eager Test and Lazy Test smells based on identifying the behavior of tests and, consequently, the behavior of the System Under Test (SUT). The approach will be evaluated with private source code repositories to identify its impact on quality attributes.
Taxing Africa for inclusive human development: the mediating role of governance quality

Purpose Given that the literature on the links between taxation and inclusive human development is ambiguous, it is important to investigate whether the mediating influence of governance in taxation for inclusive development exists. Thus, this study aims to explore the linkages between the governance quality, taxation and inclusive human development (i.e. inequality-adjusted human development index). Design/methodology/approach This study employs the generalized method of moments (GMM) technique to establish the empirical findings on 52 African countries for the period 2010–2018. Among the existing GMM approaches, this study follows the Roodman approach, an enhancement of the Arellano and Bover techniques, which limits the proliferation of instruments. This study uses the two-step approach, which deals with issues of the heteroscedasticity as against instead the one-step procedure, which solely addresses the homoscedasticity concerns. Findings The following findings are established. First, there is an unconditional positive effect of taxation on inclusive human development. Second, the net effects of taxation on inclusive human development, associated with the interaction of the government revenue with governance quality variables, are positive for the most part. It is then evident that when taxation policies are combined with good governance initiatives, the ultimate impact of inclusive human development is likely to be enhanced. Originality/value This study establishes that, whereas taxation dynamics largely have a favorable incidence in promoting inclusive human development, when such taxation measures are complemented with good governance initiatives, the overall impact of inclusive human development is also likely to be positive. It follows that policies designed to promote political, economic and institutional governance should be implemented in tandem, which policies designed to boost tax performance in the sampled countries. The findings can also be understood from the perspectives that inclusive human development is likely to be boosted when taxation measures are complemented with, (1) the free and fair election and replacement of political leaders (i.e. political governance), (2) the formulation and implementation of inclusive policies for the delivery of public goods (i.e. economic governance) and (3) the respect by citizens and the state of institutions that govern interactions between them (i.e. institutional governance).
The Interplay of Servant Leader and Interpersonal Trust in Predicting Employee-Based Brand Equity: Moderating Role of Ethical Work Climate

Although servant leadership may be equipped to provide a leadership model that addresses the issues of the modern workforce, little literature is available regarding the relationship between servant leadership and employee brand-based equity. This study contends to address this gap for which data have been collected from the service industry under a cross-sectional research design by distributing 410 questionnaires among the participants, out of which 337 were received back. After discarding the partially filled and incomplete responses, the useable responses were 314. Data were analyzed via the Smart PLS approach by applying the structural equation modeling technique. Results indicate that servant leadership directly increased the employee-based brand equity by the mediating role of interpersonal trust. However, this study has not established the moderating role of an ethical work climate.
Estimating 3D spatiotemporal point of regard: a device evaluation

This paper presents and evaluates a system and method that record spatiotemporal scene information and location of the center of visual attention, i.e., spatiotemporal point of regard (PoR) in ecological environments. A primary research application of the proposed system and method is for enhancing current 2D visual attention models. Current eye-tracking approaches collapse a scene's depth structures to a 2D image, omitting visual cues that trigger important functions of the human visual system (e.g., accommodation and vergence). We combined head-mounted eye-tracking with a miniature time-of-flight camera to produce a system that could be used to estimate the spatiotemporal location of the PoR-the point of highest visual attention-within 3D scene layouts. Maintaining calibration accuracy is a primary challenge for gaze mapping; hence, we measured accuracy repeatedly by matching the PoR to fixated targets arranged within a range of working distances in depth. Accuracy was estimated as the deviation from estimated PoR relative to known locations of scene targets. We found that estimates of 3D PoR had an overall accuracy of approximately 2° omnidirectional mean average error (OMAE) with variation over a 1 h recording maintained within 3.6° OMAE. This method can be used to determine accommodation and vergence cues of the human visual system continuously within habitual environments, including everyday applications (e.g., use of hand-held devices).
Improving Automated Crash Reproduction

Fixing bugs is a lengthy process which currently requires several manual steps to be undertaken by a developer. Reproducing a crash often takes a significant amount of time during this process, as it requires a developer to identify where the crash occurred and where the taint began, thus leading to the crash. Several tools, such as EvoCrash, STAR, and Beacon, have been created to automate this process. Proposed research includes creating a benchmark dataset, performing an empirical evaluation of fitness functions for crash reproduction, and combining both evolutionary and static approaches to reduce search spaces and increase the effectiveness of automated crash reproduction tools.
A nonparametric estimation method for the multivariate mixture models

In this paper we study the estimation of the mixing proportions and component density functions for a nonparametric multivariate mixture model that satisfies the condition of identifiability. We propose a new estimation method which combines the advantages of the matrix simultaneous diagonalization method and the basis method. The consistency of the estimator is proved and its convergence rate is derived. Simulations are conducted to evaluate the performance of the proposed method. The method is also applied to the US communities and crime data set. Numerical simulations and real data analysis show the good performance with low computational cost of the new method in the estimation for the multivariate mixture models.
Efficiently Enumerating Answers to Ontology-Mediated Queries

We study the enumeration of answers to ontology-mediated queries (OMQs) where the ontology is a set of guarded TGDs or formulated in the description logic ELI and the query is a conjunctive query (CQ). In addition to the traditional notion of an answer, we propose and study two novel notions of partial answers that can take into account nulls generated by existential quantifiers in the ontology. Our main result is that enumeration of the traditional complete answers and of both kinds of partial answers is possible with linear-time preprocessing and constant delay for OMQs that are both acyclic and free-connex acyclic. We also provide partially matching lower bounds. Similar results are obtained for the related problems of testing a single answer in linear time and of testing multiple answers in constant time after linear time preprocessing. In both cases, the border between tractability and intractability is characterized by similar, but slightly different acyclicity properties.
Machine Vision-Based Ping Pong Ball Rotation Trajectory Tracking Algorithm

Because of the overwhelming characteristics of computer vision technology, the trend of intelligent upgrading in sports industry is obvious. Video technical and tactical data extraction, big data analysis, and match assistance systems have caused profound changes to all aspects of the sports industry. One of the important applications is the playback and analysis of sports videos. People can observe the videos and summarize the experience of sports matches, and in this process, people prefer the computers to also interpret and analyze sports matches, which can not only help coaches in postmatch analysis but also design robots to assist in teaching and training. In this paper, we have examined and designed an automatic detection system for ping pong balls, in which the motion trajectory and rotation information of ping pong balls are mainly detected. To achieve this goal, the detection and tracking algorithm of ping pong balls based on deep neural network is used, and better results are achieved on the data set established by ourselves and the actual system test. After obtaining the position of the ping pong ball in the image, the rotation direction and speed of the ping pong ball are calculated next, and the Fourier transform-based speed measurement method and the CNN-based rotation direction detection method are implemented, which achieve better results in the testing of lower speed datasets. Finally, this paper proposes an LSTM-based trajectory prediction algorithm to lay the foundation for the design of table tennis robot by predicting the trajectory of table tennis. Experimental tests show that the proposed system can better handle the ping pong ball tracking and rotation measurement problems.
Algorithm based on the 1-norm character of interferograms for quantitative differential interference imaging

Differential interference contrast (DIC) microscopy is widely used as a high-contrast imaging method for observing transparent biological samples. However, the absence of fringes in DIC images results in phase gradients that cannot be effectively extracted by most phase-shift algorithms, limiting its application in quantitative detection. Therefore, we propose an algorithm based on the 1-norm character of interferograms that can accurately extract phase shifts from a series of interferograms with less than 1/2 fringes. We improved this algorithm by combining the calculated phase shift with the least-squares fitting method to further extract the phase and applied it to quantitative DIC imaging. In contrast to the conventional algorithm, this algorithm can extract the phase gradient in DIC images with unknown phase shifts, thereby eliminating the computational error in the conventional algorithm introduced by imprecise phase shifts. Simulations and experiments demonstrated that the proposed algorithm can perform accurate phase gradient extraction.