Reviewerly/exHarmony huggingface dataset:
USAGE OF VISUAL PROGRAMMING FOR DEVELOPMENT OF MATHEMATICAL SKILLS OF STUDENTS

The paper considers modern approaches to mathematical education, programming, which are used during the organization of the educational process in educational institutions. One of the directions for improving the quality of mathematical education is the use of new approaches and teaching methods, which are implemented in close interaction with the latest computer technologies. Modern rapid development of programming languages and online environments gives teachers a unique opportunity to expand their arsenal of methods, forms and means of teaching, used in pedagogy. Visualization of the learning process, the usage of game methods contributes to the development of competency skills of students, both schoolchildren and students. Visual programming languages is one of the special training courses for computer and mathematic specialists. The students get acquainted with visual programming in the school course of computer science starting from primary school. The paper considers the special aspects of usage of visual programming in performing creative tasks aimed at the development of mathematical skills by students of these specialities. The researches of many authors on modern visualization of programming, creation of small visual games that interest students, develop their skills, in particular, mathematical, are analyzed. Attention is paid to the innovative educational technologies. Possibilities of using “Turtle” in Google Blockly and creation of a logical computer game “Labyrinth” in Scratch that allow organizing cooperation during the learning process, to make it interesting are considered. The tools for creating and using of graphic objects are offered. Some algorithms are given; examples of their application for any stage of the educational process are prepared. The main advantages of using the described environments are demonstrated. The results of the study show that Internet resources and modern information technologies significantly expand the potential of educational material, contribute to the development of student motivation and differentiation of methods, forms and means of learning, taking into account the individual characteristics of students.
A Process to Detect Exploitation and Exploration Student’s Behaviors in Higher Vocational Education

the article presents a process for collecting at runtime and then classifying a rich set of students’ tracks. Considering the learning situation and the facets of interest from which we want to classify the students’ behaviors, we detail the objectives and the different steps of such a process. This data collected in the framework of vocational Education at the University level enabled us to conduct some supervised classifications that we are using in our works towards a Recommender System fitted to the students’ needs.
Basic Searching and Sorting

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Effective Motion planning and Navigation for Reconfigurable Mobile Robots using Modified A* Algorithm

Robotic systems that can transform their structure while in operation are known as reconfigurable robots. The additional configuration flexibility makes them ideal for a variety of industrial applications involving motion planning, such as sanitizing, space vehicle maintenance, and surveillance. Every floor cleaning robot needs to have a coverage route planning strategy. Navigation and its planning in the case of a moving cleaning robot, using a grid map is the subject of this paper. An essential assumption has been made here, that the given autonomous vehicle is fully operational and has a consistent reactive navigation system. As a result, such topics are not covered in this article. The study introduces an enhancement to the conventional A* algorithm. These changes are particularly concerned with computational effort, reconfiguration time, and path efficiency. These were then tested in a simulation-based circumstance with varying degrees of environmental complexity, which demonstrated the utility of the proposed algorithm. The results show that the suggested technique showcases a good performance in terms of the area covered and the distance traveled.
Credit Card Fraud Detection Using Voter Ensemble Technique (VET)

This paper proposes to design a majority vote ensemble classifier for accurate detection of credit card frauds. There is a lack of research studies on analyzing real-world credit card data owing to the issues of confidentiality, some features were hidden. Credit Card Fraud Dataset from kaggle.com was used for this study. The dataset is made up of 284,807 the number of legitimate transactions was found to be 284,315 while the number of fraudulent transactions was found to be 492, this shows that dataset is highly imbalanced, skewed data set like this may lead to an unreliable prediction performance, which is a major classification problem. Hence this study review literatures on methods of addressing the problem imbalance in dataset, and profile a methods of using the Voter Ensemble approach to address the classification problem. The real-world credit card dataset is analyzed to check for missing values and results show that there were no missing or duplicate values in the dataset. In this study, machine learning algorithms are used to detect credit card fraud. The base learners (Logistic regression, Bagging and Naïve Bayes) and the voter ensemble methods are combined. In this experiment, data are splits into 70% training and 30 % testing, the base (learners) classifiers were trained from the training sets, and predictions evaluations were made on data using 10-fold cross-validation. Afterward, the predictions of the learned base classifiers are combined into an ensemble using a stacked generalization strategy. The experimental results positively indicate that the Voter Ensemble method achieves good performances based on Precision, Recall, F1-score and Accuracy rates in detecting fraud cases in credit cards with 95.77%, 99.99%, 97.83%, 99.99% respectively. The result is evaluated and compared with other existing works. Keywords: Voter Ensemble, Logistic Regression (LR), Naïve Bayse, Bagging, imbalance ratio (IR), CCFD.
Mitigating the Effects of Class Noise Using Two-Level Filtering Learner Algorithm

Learning with label noise imposes several consequences on learning algorithms. Since label noise causes learning algorithms to learn from corrupted data, the performance of the machine learning models drop enormously, thereby reducing prediction performance. This paper implements an integrated noise removal and training approach to filter out outliers during classifier training in order to improve the performance of the resulting classification models. The results demonstrate that the method is effective and can enhance classification accuracy significantly.
CatBoost and Genetic Algorithm Implementations for University Recommendation Systems

Graduating from a prestigious foreign university has recently become a common trend among students. Year after year, the number of students pursuing graduate and post-graduate degrees keep growing. Students choose international colleges for various reasons, including improved educational quality, increased research opportunities, higher degree value, and so forth. Additionally, getting accepted into a foreign university can be highly competitive. The students have a tough time learning about the courses and institutions available to them based on their qualifications. Private agencies are now the sole means to obtain information. This approach might be costly and time- consuming. This article intends to assist students in identifying institutions where they have a better probability of getting accepted in their graduate and/or postgraduate degrees, particularly those who wish to study at international universities. This article proposes a recommendation system leveraging the CatBoost classifier and Genetic algorithm. The experimental results of the proposed method compared to the conventional techniques for recommendation are also demonstrated in this article. Our proposed approach achieved 90.43% accuracy in predicting apt Universities.
Approximate methods, including linearization and artificial neural networks

This chapter, Approximate methods, including linearization and artificial neural networks, explores the use of approximations to make data analysis simpler, faster, and more adaptable. Taylor’s theorem is derived and used to linearize nonlinear functions. The method is used to create small number approximations, to estimate the variance of nonlinear functions, and to solve nonlinear least squares problems. The gradient of the error is identified as the key quantity controlling least squares solutions, and its further application to the gradient descent method is explored. The lookup table is introduced as an approximate method of evaluating functions of one or two variables and applied to speeding up iterative calculations. Finally, the artificial neural network is developed as an approximation technique that shares the adaptability of a lookup table while providing smoothness and enhanced flexibility. The properties of several simple network designs are illustrated, and the back-propagation algorithm for training them is derived. The power of a neural network is demonstrated by predicting the nonlinear response of a river network to precipitation.
IoT Equipment ID and Blockchain Driven Internet Fused Learning

Industrial Internet has become a burgeoning study subject as the Internet of Things (IoT), 5G, and current innovation have advanced. Because of the current claim to fame, increased requirements for effort deferment, wellness, and dependability of the distinctive proof investigation administration have been established. Because of the single kind of distinguishing proof topic and powerless acquaintance with security insurance, the conventional DNS can&#x2019;t satisfy the specifications of the new cyberspace. This paper responds by using Block Chain associated with fused learning in presentation of Internet-distinguishing-proof. BC likely to be a distributed foundation that is commonly used in computerized scrambled monetary standards, like as Bit coin, and can provide safe stoke of information with accessibility. Combined training assures particular terminal information that allows for proficient AI among various participants. Our suggested unified training and Block Chain mix offers a heavy framework towards the evolution of the upcoming contemporary Internet, according to the mathematical results.
Towards Designing Shared Digital Forensics Instructional Materials

This paper presents a systematic approach to designing a series of digital forensics instructional materials to address the severe shortage of active learning materials in the digital forensics community. The materials include real-world scenario-based case studies, a set of hands-on problem-driven labs for each case study, and an integrated forensic investigation environment. In this paper, we first clarify some fundamental concepts related to digital forensics, such as digital forensic artifacts, artifact generators, and evidence. We then re-categorize knowledge units of digital forensics based on the artifact generators for measuring the coverage of learning outcomes and topics. Finally, we utilize a real-world cybercrime scenario to demonstrate how knowledge units, digital forensics topics, concepts, artifacts, and investigation tools can be infused into each lab through active learning. The repository of the instructional materials is publicly available on GitHub. It has gained nearly 600 stars and 22k views within several months.
Reliability Analysis of Multisensor Fusion Technology in Disaster Nursing Application

The harm and impact of various natural disasters are becoming more and more obvious. Due to the high suddenness of natural disasters and the low efficiency of information sharing, our stress system will still not be able to respond to disasters in time. Therefore, facing the current complex natural environment and the diversity of disasters, we urgently need a practical and comprehensive online collaborative natural disaster protection system. Therefore, this paper proposes a disaster protection application system based on a multisensor information fusion algorithm. Firstly, the software platform of the natural disaster protection system based on multisensor is constructed; the hardware platform of multisensor disaster acquisition is designed. An information fusion algorithm based on Kalman joint filter is proposed. The main problems of joint filter are (1) improving the filtering accuracy, (2) improving the fault-tolerant performance of the filter, and (3) optimizing the fusion algorithm and reducing the amount of calculation and data communication, so as to facilitate real-time implementation of the algorithm. Combined with DS evidence theory, BP neural network, and support vector mechanism, a comprehensive diagnosis model is established. The model uses the preliminary diagnosis results of the BP neural network and support vector machine to carry out the BPA of DS evidence theory, which avoids the blindness caused by human operation. The simulation results show that the comprehensive diagnosis model can significantly improve the prediction accuracy of disasters, solve the uncertainty and fuzziness of diagnosis results of a single diagnosis model, and provide a new method for disaster protection. The results of the BP neural network in the disaster nursing system are fused at the decision level, the results show that the distribution probability corresponding to the fusion disaster category is greater than the threshold value of 0.8, and the diagnosis result can be given immediately. The diagnosis result is completely consistent with the actual disaster category.
Designing for haptics

The emergence of extended reality has brought an influx of interest in designing good haptic experiences that enhance immersion and realism, but the question is how?
Designing eXtended reality experiences for the future of hybrid meetings

Hybrid meetings are challenging. They require interface solutions that support communication between both co-located and remote team members. However, recent research on extended reality points to interesting new directions for the future of these meetings.
Resource Configuration Tuning for Stream Data Processing Systems via Bayesian Optimization

Stream data processing systems are becoming increasingly popular in the big data era. Systems such as Apache Flink typically provide a number (e.g., 30) of configuration parameters to flexibly specify the amount of resources (e.g., CPU cores and memory) allocated for tasks. These parameters significantly affect task performance. However, it is hard to manually tune them for optimal performance for an unknown program running on a given cluster. An automatic as well as fast resource configuration tuning approach is therefore desired. To this end, we propose to leverage Bayesian optimization to automatically tune the resource configurations for stream data processing systems. We first select a machine learning model—Random Forest—to construct accurate performance models for a stream data processing program. We subsequently take the Bayesian optimization (BO) algorithm, along with the performance models, to iteratively search the optimal configurations for a stream data processing program. Experimental results show that our approach improves the 99th-percentile tail latency by a factor of 2.62× on average and up to 5.26× overall. Furthermore, our approach improves throughput by a factor of 1.05× on average and up to 1.21× overall.
A Lightweight Stereo Visual Odometry System for Navigation of Autonomous Vehicles in Low-Light Conditions

Localization of vehicles in a 3D environment is a basic task in autonomous driving. In the low-light environments, it is difficult to navigate independently using a visual odometry for autonomous driving. The main reason for this challenge is the blurred images in the scenes with insufficient illumination. Although numerous works focused on this issue, it still has a number of inherent drawbacks. In this paper, we propose a lightweight stereo visual odometry system for navigation of autonomous vehicles in low-light situations. Contrary to the existing recovery methods, we aim to divide the captured image into the illumination image as well as the reflectance image and only estimate the illumination one, where the enhanced map of the low-light image is acquired by using the retinex theory. In addition, we further utilize a simplified and rapid feature detection scheme, which reduces the computation time by about 85% but maintaining the matching accuracy similar to that of ORB features. Finally, the experiments show that our average memory consumption of our proposed method is much less than the conventional algorithm.
Metrics Space and Norm: Taxonomy to Distance Metrics

A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly improve the performance of the machine learning or deep learning model in clustering, classification, data recovery tasks, etc. In this article, we provide a survey on widely used distance metrics and the challenges associated with this field. The most current studies conducted in this area are commonly influenced by Siamese and triplet networks utilized to make associations between samples while employing mutual weights in deep metric learning (DML). They are successful because of their ability to recognize the relationships among samples that show a similarity. Furthermore, the sampling strategy, suitable distance metric, and network structure are complex and difficult factors for researchers to improve network model performance. So, this article is significant because it is the most recent detailed survey in which these components are comprehensively examined and valued as a whole, evidenced by assessing the numerical findings of the techniques.
On generalized latent factor modeling and inference for high‐dimensional binomial data

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two-step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene-environment association study.
Linguistic variation in customer reviews

Abstract Informed by the framework of register analysis ( Biber &amp; Egbert 2018 ) and narrative analysis (Labov &amp; Waletzky 1967 ), this paper studies customer reviews of assistive reading devices to identify intra-register variation between narratives of one’s own and another’s experience. The lexico-grammatical features differentiating the two experience types include the use of pronouns, references to animate and inanimate entities, patterns of user and product description, stance markers, negation, as well as expectation- and transaction-related lemmas. The study reveals that the distribution of these features is determined by the narrative ownership and the associated distinctions in expectations. Furthermore, the difference in narrative ownership results in variation with respect to the experience narrative composition.
A Proactively Defensive Low-Level Decision Center Model of Endogenous Security

With the development of 5g and next-generation networks, shell-based defenses are becoming increasingly unsuitable and become a burden and obstacle to information systems. The endogenous security defense system based on the bionic mechanism has entered people's field of vision. On this basis, this paper proposes a spinal-like low-level decision center model in the endogenous security system. It uses fuzzy cognitive maps and trusted computing to comprehensively analyze the information of the system, and makes decisions based on the needs of applications and tasks. Some case studies and experimental results prove the effectiveness and efficiency of the model.
A comparison of the text types of the 2013-2022 CSAT English reading passages

As a follow-up study to J. Y. Chang (2022), the purpose of this study was to examine the text types of 250 English reading passages in the 2013-2022 College Scholastic Ability Tests (CSAT) based on Biber’s (1989) typology. The computer software Multidimensional Analysis Tagger (MAT, Version 1.3.2) (Nini, 2019) was applied to identify the text type of each passage with its dimension scores. Text types were then compared between the norm-referenced (2013-2017) and criterion-referenced (2018-2022) assessment periods. Results show that the most frequently occurring text type was involved persuasion followed by general narrative exposition, scientific exposition, and learned exposition. In particular, involved persuasion, scientific exposition, and learned exposition together accounted for 64% of the total reading passages. Between the two assessment periods, there was no significant difference in the overall distribution of the text types. However, the proportions of scientific exposition, involved persuasion, and learned exposition increased in the latter period. These results suggest that there was a tendency in the CSAT English reading passages to reflect the text types that students experience at college and measure their preparedness as college students.
PyNomo software dataset for sciences and engineering nomogram construction

This article examines different python-based codes to be run under PyNomo nomographer software to represent alignment charts or nomograms for formulas commonly used in sciences and engineering studies. PyNomo nomographer supports nine basic types of nomograms based on the format of the mathematical equation. The examined PyNomo codes cover the construction of the following types of nomogram: type 1, type 2, type 7, type 8, type 9, and type 10. Each PyNomo script includes some background and the underlying mathematics for the nomogram construction and how to implement each code into PyNomo software. Potential users can use these PyNomo codes, as template scripts, to customize and construct their own nomograms for a variety of technical applications. That is especially true since a considerable number of science and engineering equations fall into one of these covered nomogram types depending on the equation form involved.