Electronic voting, based on cryptographic protocols and blockchain technology, represents an advanced method for ensuring the security, transparency, and reliability of electoral processes. The scientific research presented in this article is devoted to the analysis of trends and advanced technologies in the field of electronic voting. The authors propose a systematic mapping of this area using bibliometric analysis. The research is based on the analysis of scientific articles extracted from the Web of Science and Scopus databases. Bibliometric analysis tools are used to automatically identify key topics and trends in the literature. The results of the study reveal the key directions of the development of electronic voting, including the integration of cryptographic protocols and blockchain technology. Specific aspects include improving privacy through the use of modern cryptographic protocols, creating an immutable register of votes using blockchain technology, ensuring trust through open-source code, and increasing resilience to cyber-attacks. These areas reflect the desire to develop efficient, transparent, and secure electronic voting systems. Based on the identified trends, recommendations are proposed for further research aimed at improving electronic electoral systems and their adaptation to the modern requirements of security and transparency in electoral processes.

Low-power microcontrollers, wireless sensors, and embedded gateways form the backbone of many Internet of Things (IoT) deployments. However, their limited memory, constrained energy budgets, and lack of standardized firmware make them attractive targets for diverse attacks, including bootloader backdoors, hardcoded keys, unpatched CVE exploits, and code-reuse attacks, while traditional single-layer defenses are insufficient as they often assume abundant resources. This paper presents a Systematic Literature Review (SLR) conducted according to the PRISMA 2020 guidelines, covering 196 peer-reviewed studies on cross-layer security for resource-constrained IoT and Industrial IoT environments, and introduces a four-axis taxonomy—system level, algorithmic paradigm, data granularity, and hardware budget—to structure and compare prior work. At the firmware level, we analyze static analysis, symbolic execution, and machine learning-based binary similarity detection that operate without requiring source code or a full runtime; at the network and behavioral levels, we review lightweight and graph-based intrusion detection systems (IDS), including single-packet authorization, unsupervised anomaly detection, RF spectrum monitoring, and sensor–actuator anomaly analysis bridging cyber-physical security; and at the policy level, we survey identity management, micro-segmentation, and zero-trust enforcement mechanisms supported by blockchain-based authentication and programmable policy enforcement points (PEPs). Our review identifies current strengths, limitations, and open challenges—including scalable firmware reverse engineering, efficient cross-ISA symbolic learning, and practical spectrum anomaly detection under constrained computing environments—and by integrating diverse security layers within a unified taxonomy, this SLR highlights both the state-of-the-art and promising research directions for advancing IoT security.

This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.

The paper presents research findings related to the security of widespread Internet of Things (IoT) platforms and protocols, based on the analysis of existing vulnerability databases and practical penetration testing of IoT device networks using modern methods. An algorithm for collecting and extracting relevant data from the Common Vulnerabilities and Exposures (CVE) database has been developed. Additionally, a web interface for an interactive CVE table was created, facilitating the processing and visualization of large volumes of data in a convenient and clear format. The conducted analysis aimed to identify the most critical vulnerabilities that should be minimized through the introduction of a new authentication protocol for IoT devices and to define specific requirements for the protocol targeting identified vulnerabilities. During penetration testing of wireless networks, significant vulnerabilities were discovered in the examined IoT devices and the LoRaWAN wireless network. As a result, a dataset containing a list of IoT system vulnerabilities extracted from the CVE glossary, including descriptions of attack vectors and severity based on the Common Vulnerability Scoring System (CVSS), was compiled, along with practical recommendations for mitigating the vulnerabilities.

Electronic voting requires the simultaneous admission of only legitimate participants, ballot uniqueness, vote confidentiality, storage integrity, and result verifiability. Blockchain alone does not solve these problems, since ledger immutability does not guarantee anonymity, ballot correctness, or reduced trust concentration. The purpose of this work is to develop a parameterizable research framework for electronic voting scenarios with enhanced cryptographic protection, allowing the security level to be varied according to the requirements of a voting scenario. The main contribution of the work is a parameterizable research architecture for composing and experimentally comparing electronic voting configurations with different security and computational profiles. The cryptographic and audit mechanisms integrated into this architecture include blind-signature-based anonymous authorization, encrypted ballot submission, blockchain-style audit, receipt verification, homomorphic tally publication, and threshold-supported tally artifacts. These mechanisms are not proposed as new cryptographic primitives; rather, they are integrated into a reproducible prototype to study how their combination affects verifiability, privacy support, auditability, and computational cost. Compared with basic blockchain-based voting prototypes, this architecture explicitly separates security, privacy, and verifiability profiles and makes their computational cost observable. The implemented prototype is used as an experimental platform for analyzing supported security properties, threat modeling, and computational cost estimation. The results show that authentication, anonymous token issuance, and receipt verification maintain an almost constant cost at the studied scale, while the main cryptographic burden is associated with encrypted ballot submission and threshold-supported tally publication. The scientific novelty of the work lies in constructing a parameterizable architecture that integrates several cryptographic mechanisms and a blockchain audit layer into one reproducible research prototype. At the same time, the proposed approach retains prototype-level limitations associated with the absence of a full zero-knowledge proof stack, independently deployed threshold authorities, and coercion-resistance mechanisms.

In this paper, we present a review of the latest deep learning methods used in inspecting railway infrastructure with vision systems. Traditional methods for inspecting railways are not always efficient, scalable or accurate, so using computer vision and artificial intelligence is now needed to maintain rail safety. The research divides more than 200 papers into important themes, covering rail surface fault detection, issues with fasteners and sleepers, adding synthetic data, using different types of railroad sites and multi-technology integration. For every domain, we cover current progressive models, for example, CNNs, YOLOv5, U-Net, Faster R-CNN and Vision Transformers, discussing their uses, pros and cons. The problem of using the model in practice is analyzed by studying generalization, instant results, understanding how it learns and the hardware limitations. We also find areas where progress is needed and suggest future solutions to reduce the difference between what academic discovery and what industries can use.