The proliferation of real-time, infinite data streams necessitates efficient online learning approaches. Hoeffding Trees (HT), which extend traditional decision trees using the Hoeffding bound, offer robust stream classification but face high computational costs. While the Green Accelerated Hoeffding Tree (GAHT) addresses energy efficiency concerns, its prediction accuracy can be improved by addressing its inherent limitations in combining Hoeffding bounds with information gain metrics for incrementally growing the tree. This study successfully develops enhanced GAHT variants through optimized Hoeffding bound stability and node splitting mechanisms. Our empirical evaluation demonstrates that the usage of these new variants improves predictive performance over the state-of-the-art GAHT, without compromising its energy efficiency.

Processing texts from distant historical periods, especially those handwritten in languages with low computational resources, presents significant challenges. Even if modern methods make it possible to achieve, after laborious machine learning procedures, a fairly good rate of correct character recognition, the problem of the correctness of the resulting editable text remains a topical one. This paper presents an approach that contributes to the automation of the PostOCR proofreading process based on the presentation of digitized text using historical fonts, similar to those in the original document.

Gait, an individual's unique walking style, serves as an effective biometric tool for surveillance. Unlike fingerprints or iris scans, gait is observable from a distance without the subject's awareness, making it ideal for security applications. CNNs struggle with video variability, affecting gait recognition. This study introduces GaitDeep, a spatial-temporal refinement using a deep dense network. It integrates attention-enhanced spatial extraction with a two-directional LSTM-based temporal module to prioritize key segments. Evaluated on the OU-ISIR, OU-MVLP, and CASIA-B datasets, GaitDeep achieves accuracies of 95.1%, 0.96%, and 98.10%, respectively, outperforming state-of-the-art methods and establishing a new benchmark for gait recognition.

The study examines possible solutions to resolve problems that occur in the process of operationalizing systems that predict the financial services that customers will terminate using machine learning and business intelligence approaches. The scope of the study consists of defining the problem, collecting and integrating the data, training the models, evaluating and validating the outputs, and making them ready for use in the production environment. In addition, intuition about infrastructure, architecture, processes, technologies, and other artifacts used during the study is included. The data manipulation and pre-processing framework proposed in this study is applicable to both real and synthetic banking data. To implement each step in detail, an improved version of an auxiliary study was used. A study has been carried out in a financial institution in Turkey, chosen as an auxiliary, in which customers who are likely to cancel credit cards are determined by machine learning. The problems, findings, and results are examined in detail. The framework used in this study is believed to be used not only in the integration of credit card product churn detection systems but also in the integration of other systems that use machine learning and deep learning.

The present research focuses on the security of Macedonian websites. It involves the analysis of HTTP Security header responses for 756 websites in the country, of which 246 are the most popular. This analysis is conducted across 13 different categories of websites, including government bodies and institutions, public institutions and enterprises, educational, commercial, news and media, entertainment, sports, etc. We intend to create a comprehensive security profile for the country's websites, which will help raise their overall security level. It is critical to understand and implement proper HTTP security headers to prevent or limit the dangers that can cause website attacks such as Denial of Service (DoS), Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), SQL Injection, clickjacking, etc. Our analysis was performed with the help of the Mozilla Observatory tool. We have discovered a significant lack of implementation and/or misconfiguration of HTTP security headers in all categories. Almost half of the websites (n=375; 49.60\%) have an F grade, while more than a quarter of all websites (n=214; 28.31\%) have a minimal security score of 0.

Our paper focuses on Linked Data Lineage as the collection of modeled organizational data, enabling continuous integration and modern governance in Business-to-Business (B2B) sharing. We first explore approaches organizations adopt to enhance B2B data integration. Next, we highlight key B2B and Business-to-Government (B2G) linked data use cases and strategies for governance frameworks. We review software tools supporting data governance and propose a systemic method for defining organizational integration flows. This is demonstrated through a system enabling businesses to design linked data pipelines, leveraging Semantic Web technologies for continuous integration and governance. Insights and recommendations for improving data sharing processes are provided.

We construct a 1-tape 98-state 10-symbol universal reversible Turing machine (URTM(98,10)) that directly simulates reversible counter machines (RCMs). The objective of this construction is not to minimize the numbers of states and tape symbols, but to give a URTM a reasonable size whose simulating processes of RCMs are easily understood. Here, we choose RCMs as the target machines of simulation, since the class of RCMs is known to be Turing universal, and their operations are very simple. Furthermore, using the framework of RCMs in the program form (rather than the quadruple form), construction of a URTM is simplified. We also created a computer simulator for the URTM(98,10), by which simulation processes of RCMs are visualized.

This paper offers a comprehensive retrospective on the development and results in the field of insertion-deletion systems over the past 15 years, building upon an earlier foundational overview from 2010. We review key theoretical developments, new extensions, and variations that have emerged, examining their impact on the computational power of insertion-deletion systems. This retrospective aims to update the field’s understanding of the model, providing insights into the latest progress and identifying promising directions for future research.

In recent years, image watermarking has become the most active research in the information hiding field. This paper presents an application of a computational intelligence technique to obtain robust and secure image-watermarking schemes for data image transmission. The optimization goal is achieved via a new robust method based on the hybridization (in the digital domain) of the image-watermarking scheme using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The Forensic-Based Investigation (FBI) optimization algorithm is considered to search for the best scaling factor and the appropriate location for the watermark insertion. The Arnold Transform (AT) encryption is also introduced to enhance the watermark image process security. To show the efficiency of the proposed schemes, several watermarked images are subjected to several attacks, and their imperceptibility and robustness are checked via structural similarity index measure (SSIM) and Normalized Correlation Coefficient (NCC) where the proposed schemes provide competitive simulation results. Statistical analysis is also carried out to prove the performance of the used optimization algorithm.

As commonly understood, Augmented Reality (AR) technology, with its potential to blend digital content into the real world, has gained significant attention in various fields. However, its implementation is not without hurdles. Embracing AR comes with a set of challenges that cover technical, usability, ethical, and practical considerations. It may happen that the augmentation doesn’t consistently yield the desired outcomes, work as initially intended, or deliver a seamless and satisfying user experience. In this article, we present the standards in the development and adoption of AR, also solutions to enhance augmented experiences based on our two years of developing educational applications.