Search

Modern software systems consist of many software components; the source code of modern software systems is hard to understand and maintain for new developers. Aiming to simplify the readability and understandability of source code, companies that specialize in software development adopt programming standards, software design patterns, and static analyzers with the aim of decreasing the complexity of software. Recent research introduced a number of code metrics allowing the numerical characterization of the maintainability of code snippets. Cyclomatic Complexity (CycC) is one widely used metric for measuring the complexity of software. The value of CycC is equal to the number of decision points in a program plus one. However, CycC does not take into account the nesting levels of the syntactic structures that break the linear control flow in a program. Aiming to resolve this, the Cognitive Complexity (CogC) metric was proposed as a successor to CycC. In this paper, we describe a rule-based algorithm and its specializations for measuring the complexity of programs. We express the CycC and CogC metrics by means of the described algorithm and propose a new complexity metric named Educational Complexity (EduC) for use in educational digital environments. EduC is at least as strict as CycC and CogC are and includes additional checks that are based on definition-use graph analysis of a program. We evaluate the CycC, CogC, and EduC metrics using the source code of programs submitted to a Digital Teaching Assistant (DTA) system that automates a university programming course. The obtained results confirm that EduC rejects more overcomplicated and difficult-to-understand programs in solving unique programming exercises generated by the DTA system when compared to CycC and CogC. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of detecting cancer using such biomarkers as blood protein markers. The purpose of this research is to propose an approach for making decisions in the diagnosis of cancer through the creation of cost-sensitive SVM classifiers on the basis of datasets with a variety of features of different nature. Such datasets may include compositions of known features corresponding to blood protein markers and new features constructed using methods for calculating entropy and fractal dimensions, as well as using the UMAP algorithm. Based on these datasets, multiclass SVM classifiers were developed. They use cost-sensitive learning principles to overcome the class imbalance problem, which is typical for medical datasets. When implementing the UMAP algorithm, various variants of the loss function were considered. This was performed in order to select those that provide the formation of such new features that ultimately allow us to develop the best cost-sensitive SVM classifiers in terms of maximizing the mean value of the metric M a c r o F 1 − s c o r e . The experimental results proved the possibility of applying the UMAP algorithm, approximate entropy and, in addition, Higuchi and Katz fractal dimensions to construct new features using blood protein markers. It turned out that when working with the UMAP algorithm, the most promising is the application of a loss function on the basis of fuzzy cross-entropy, and the least promising is the application of a loss function on the basis of intuitionistic fuzzy cross-entropy. Augmentation of the original dataset with either features on the basis of the UMAP algorithm, features on the basis of the UMAP algorithm and approximate entropy, or features on the basis of approximate entropy provided the creation of the three best cost-sensitive SVM classifiers with mean values of the metric M a c r o F 1 − s c o r e increased by 5.359%, 5.245% and 4.675%, respectively, compared to the mean values of this metric in the case when only the original dataset was utilized for creating the base SVM classifier (without performing any manipulations to overcome the class imbalance problem, and also without introducing new features). [ABSTRACT FROM AUTHOR]

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students. Dataset: The data presented in this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.7799971 (accessed on 10 June 2023). Dataset License: CC-BY-4.0 [ABSTRACT FROM AUTHOR]

In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of the proposed hybrid intellectual data classification technology have been presented.

The problem of the data analysis in the educational sphere in the context of prediction of the passing's success of the final state attestation by the graduates of the secondary school has been considered. Such data can be imbalanced substantially. To solve this problem it is offered to use the SVM classifiers on the base of the modified PSO algorithm, which allows choosing the kernel function type, the values of the kernel function parameters and the value of the regularization parameter simultaneously. In advance, the different rebalancing strategies, based on the basic SMOTE algorithm, can be applied for rebalance the classes in the experimental datasets. The prediction results with the use of the SVM classifiers on the base of the modified PSO algorithm and the different rebalancing strategies have been presented and compared with the prediction results received on the base of the most known software packages, such as Statistica StatSoft and IBM SPSS Modeler.

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

The approach to the classification problem of the imbalanced datasets has been considered. The aim of this research is to determine the effectiveness of the SMOTE algorithm, when it is necessary to improve the classification quality of the SVM classifier, which is applied for classification of the imbalanced datasets. The experimental results which demonstrate the improvement of the SVM classifier quality with application of ideas the SMOTE algorithm for the imbalanced datasets in the sphere of medical diagnostics have been given.

The identification algorithms, which carry out the objects’ identification of the Earth’s surface by means of their hyperspectral features’ analysis, received on the base of the processed space images from the „Resource-P” spacecrafts with application of the similarity measures, have been considered. The identification algorithms on the base of the Euclidean distance similarity measure, the angular similarity measure and the fuzzy similarity measure have been applied. The use expediency of the fuzzy linear regression in the algorithm of objects’ hyperspectral features’ identification has been shown. The results of the hyperspectral information processing with using of the offered algorithms have been presented.

The article discusses the possibility of using fuzzy masks in the problem of image georeferencing according to electronic maps. A new approach to membership assessment of the image fragment in fuzzy mask has been suggested. Fuzzy masks have been compared with binary masks by the accuracy of coregistration of image fragments and coastlines extracted from electronic maps. It’s shown that fuzzy masks allow taking into account brightness characteristics of water and land in different regions of the Earth at different times of day and year. The results of experimental studies confirm the efficiency of the offered approach to image georeferencing based on the fuzzy masks.

The problem of improving the classification accuracy using the SVM classifier ensemble has been considered. This paper defines the rules for the selection of individual SVM-classifiers used in the future for the creation of an ensemble and the strategies for the integration of ensemble members.

The paper describes the aspects of the augmented reality application development on the base of ARToolkit for Android. The steps consistently used for the augmented reality application development have been considered. The simplest Java-based examples of augmented reality have been discussed.

The algorithm which carries out the multiobjective optimization at realization of the modified clonal selection algorithm based on the use of the notion «Pareto set» when the parental population of antibodies should be created for development of the forecasting models on the base of the strictly binary trees has been offered. Two indicators of quality of the forecasting model – the affinity indicator based on the calculation of the average forecasting error rate, and the tendencies discrepancy indicator – are applied in the role of the objective functions. The results of experimental studies which confirm the efficiency of application of the offered algorithm have been given.

The paper describes the main aspects of application of the software package STATISTICA in the educational process. Technologies of data mining which can be useful for students researches have been considered. The main tools of these technologies have been discussed.