Acta Informatica Pragensia - Forthcoming articles

ResNetMF: Improving Recommendation Accuracy and Speed with Matrix Factorization Enhanced by Residual Networks

Mustafa Payandenick, YinChai Wang, Mohd Kamal Othman, Muhammad Payandenick

Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.28011  

Background: Recommendation systems are essential for personalized user experiences but struggle to balance accuracy and efficiency.Objective: This paper presents ResNetMF, an innovative hybrid framework designed to address these limitations by combining the strengths of matrix factorization (MF) and deep residual networks (ResNet). Matrix factorization excels at capturing explicit linear relationships between users and items, while ResNet is employed to model non-linear residuals.Methods: By focusing on refining the baseline MF output through incremental improvements, ResNetMF minimizes redundant computations and significantly enhances recommendation...

Analysis of Benford’s Law Conformity with Web of Science Citations of Documents

David Jiri Slosar

Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.281248  

Background: Benford’s law is a statistical phenomenon that predicts the probability of a particular digit at a particular position in a number. This law has been successfully applied in a number of areas, such as accounting. In the area of scientometrics, research has been devoted mostly to journal data.Objective: This paper investigates the conformity of Benford’s law with the citation counts of records retrieved from the Web of Science database. We evaluate the conformity levels with Benford’s law in the complete dataset. We determine the effect of document type (article, proceedings paper and review), year of publication (2014–2018)...

Personalized Learning Analytics Through Static Code Analysis in Computer Science Education

Marek Horváth, Emília Pietriková, Filip Gurbáľ

Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.283106  

Background: Learning programming is often difficult for beginners, primarily because of the challenge of providing timely and personalized feedback in large educational environments. While automated assessment systems have improved efficiency in grading and feedback, they typically focus on correctness and often lack personalized guidance concerning code quality, readability, and maintainability.Objective: This study aims to investigate whether integrating static code analysis into automated assessment systems to provide personalized feedback can effectively enhance students code quality, learning process, and engagement in programming courses.Methods:...

Automated Machine Learning in Action: Performance Evaluation for Predictive Analytics Tasks

Nicolas Leyh

Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.28874  

Background: As organizations increasingly seek data-driven insights, the demand for machine learning (ML) expertise outpaces the current workforce supply. Automated Machine Learning (AutoML) frameworks help close this gap by streamlining the ML pipeline, making advanced modeling accessible to non-specialists.Objective: This study evaluates the performance of four open-source AutoML frameworks—Auto-Keras, Auto-Sklearn, H2O, and TPOT—in predictive analytics, focusing on both binary and multiclass classification. The goal is to identify performance strengths and limitations under varying dataset conditions and propose improvements for framework...