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Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

Homomorphic encryption enables computations to be performed directly on encrypted data, ensuring data confidentiality even in untrusted or distributed environments. Although this approach provides strong theoretical security, its practical adoption remains limited due to high computational and memory requirements. This study presents a comparative evaluation of three representative homomorphic encryption paradigms: partially, somewhat, and fully homomorphic encryption. The implementations are based on the GMP library, Microsoft SEAL, and OpenFHE. The analysis examines encryption and decryption time, ciphertext expansion, and memory usage under various parameter configurations, including different polynomial modulus degrees. The goal is to provide a transparent and reproducible comparison that illustrates the practical differences among these approaches. The results highlight the trade-offs between security, efficiency, and numerical precision, identifying cases where lightweight schemes can achieve acceptable performance for latency-sensitive or resource-constrained applications. These findings offer practical guidance for deploying homomorphic encryption in secure cloud-based computation and other privacy-preserving environments.

This paper presents a cloud-based facial biometric authentication system integrating demographic and emotional attributes to enhance verification robustness under compression and distortion. A prototype web platform was implemented using cloud-based services, combining facial recognition with demographic and emotional analysis. The system was experimentally evaluated under multiple image degradation scenarios, including lossy and lossless compression, blurring, and block partitioning. Results show that recognition accuracy remains above 98% under mild compression, but decreases significantly under severe distortions, with match rates dropping below 25% in extreme cases. Additional experiments demonstrate reliable gender classification (95%) and moderate performance for age and emotion estimation. The contribution of this work lies in demonstrating the practical feasibility of combining identity verification with contextual biometric features in resource-constrained environments, while also outlining the limitations and security implications of such systems.

The further development of human–computer interaction applications is still in great demand as users expect more natural interactions [...]

The paper aims to study speaker and listener behavior in dyadic speech communication. A multimodal (speech and video) corpus of dyadic face-to-face conversations on various topics was created. The corpus was manually labeled on several layers (text transcription, backchannel modality and function, POS tags, prosody, and gaze). The statistical analysis was done on the proposed corpus. We focused on backchannel inviting cues on the speaker side and backchannels on the listener side and their patterns. We aimed to study interlocutor backchannel behavior and backchannel-related signals. The results of the analysis show similar patterns in the case of backchannel inviting cues between Slovak and English data and highlight the importance of gaze direction in a face-to-face speech communication scenario. The described corpus and results of the analysis are one of the first steps leading towards natural artificial intelligence-driven human–computer speech conversation.

This article compares EVE-NG, a network emulation software, to other commonly used network simulation tools such as Cisco Packet Tracer and GNS3. EVE-NG’s features, benefits, and drawbacks are assessed in terms of system requirements, support platforms, web interface, console access, topology management, and advanced lab functionalities. EVE-NG is tested by the students in the real-world application, and feedback from a group of students is gathered regarding their satisfaction and suggestions for improvement. The results of this study show that EVE-NG provides a robust and versatile tool for simulating complex network scenarios, despite compatibility, reliability, and performance issues. It is concluded that EVE-NG has the potential to improve student learning outcomes in computer networks; however, ongoing development and refinement are necessary to meet user expectations and needs.

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

The limited availability of language resources for Slovak presents a significant challenge for the development and evaluation of language models. In this paper, we introduce a multiple-choice question-answering dataset specifically designed for the financial domain in Slovak. The dataset contains 1,334 questions, each with one correct answer and four incorrect ones. It is systematically organized by topic and difficulty level to facilitate structured evaluation. Using this dataset, we assess the performance of several Slovak generative language models and compare their results against a general question-answering dataset to analyze domain-specific model capabilities. The best-performing model is a monolingual Slovak model. Furthermore, the observed performance differences between financial-domain and general question-answering tasks suggest that domain-specific language modeling requires further research.

COVID-19-related quarantine measures resulted in a significant increase of interest in online collaboration tools. This includes virtual reality (VR) or, in more general term, extended reality (XR) solutions. Shared XR allows for activities such as presentations, training of personnel or therapy to take place in a virtual space instead of a real one. To make online XR as accessible as possible, a significant effort has been put into the development of solutions that can run directly in web browsers. One of the most recognized solutions is the A-Frame software framework, created by Mozilla VR team and supporting most of the contemporary XR hardware. In addition, an extension called Networked-Aframe allows multiple users to share virtual environments, created using A-Frame, in real time. In this article, we introduce and experimentally evaluate three components that extend the functionality of A-Frame and Networked-Aframe. The first one extends Networked-Aframe with the ability to monitor and control users in a shared virtual scene. The second one implements six degrees of freedom motion tracking for smartphone-based VR headsets. The third one brings hand gesture support to the Microsoft HoloLens holographic computer. The evaluation was performed in a dedicated local network environment with 5, 10, 15 and 20 client computers. Each computer represented one user in a shared virtual scene. Since the experiments were carried out with and without the introduced components, the results presented here can also be regarded as a performance evaluation of A-Frame and Networked-Aframe themselves.

Children’s speech differs significantly from adult speech due to physiological and cognitive developmental factors. Key differences include higher pitch, a shorter vocal tract, greater formant frequencies, slower speaking rates, and greater variability in pronunciation and articulation. These differences result in acoustic mismatches between children’s and adult speech, making traditional automatic speech recognition models trained on adult speech less effective for children. Additionally, linguistic differences, such as limited vocabulary and evolving grammar, further contribute to this challenge. This paper focuses on the creation of a children’s speech database for the low-resource Slovak language. This database has been used to train acoustic models for the automatic recognition of spontaneous children’s speech in Slovak. In this research, we compared three different approaches to speech recognition, with self-supervised learning achieving results comparable to similar studies in this area, despite using relatively small amounts of training data.