The Polish Naval Academy (PNA) "Heroes of Westerplatte" is a naval university supervised by the Ministry of National Defence of the Republic of Poland, with the history, uninterrupted by World War II, dating back to 1922. At present the PNA provides education for officer-cadets, commissioned officers and civilian students at first and second cycles of study (undergraduate and graduate), as well as doctoral studies. It also offers opportunities for professional development at specialized courses and postgraduate programs. In accordance with international agreements the PNA trains officers for naval forces of countries in Europe, North Africa, the Middle and Far East. International exchange significantly contributes to the rise in qualifications of the PNA staff. It also allows the students to attend lectures given by best specialists from leading scientific centers of the world.
The paper addresses the possibility of non-military threats occurring in sea areas and assessing their trend. The paper unequivocally indicates that in peaceful conditions of maritime operations, the risk of crisis situation occurrence is highly probable and may even increase in the future. Therefore, a brief review of events and the current safety situation in sea areas was first carried out. Based on already defined phenomena, a trend, and a catalog of predicted risks of a non-military character in the sea regions were developed. The final effect of the paper will be to indicate the trend of current non-military threats, define a catalog of anticipated risks in the sea areas, and indicate areas of particular sensitivity, i.e., what to put the most significant emphasis on to prepare for the occurrence of a given risk and what are the possibilities of reducing it to an acceptable level or even reversing the trend of increasing threats.
This article challenges deficit-oriented narratives that portray adolescents’ engagement with digital media as risk or decline. Drawing on a two-year ethnographic study in Polish upper-secondary schools, it analyses how young people negotiate visibility in postdigital educational environments. Integrating perspectives from Hannah Arendt, Jacques Rancière and Shoshana Zuboff, the article conceptualises adolescent agency at the intersection of action, recognition, affect and algorithmic governance. Rather than off-task behaviour, students’ digital practices appear as attempts to become subjects of knowledge. The findings suggest that the so-called “e-generation crisis” reflects a misalignment between educational frameworks and postdigital conditions of learning. The study is based on two upper-secondary school classes and does not claim systemic representativeness; its aim is to identify and interpret recurring mechanisms and tensions rather than to generalise across the educational system.
In this article, we present the results of a study of the use of selected artificial neural network architectures for the classification of marine objects generating acoustic signals. The training data were acquired from various types of platform, including manned underwater vehicles, autonomous underwater vehicles, diver propulsion vehicles, surface vessels, and high-speed motorboats. A total of 14 models were trained and evaluated on two complementary datasets, consisting of a simulated submarine dataset and a real-world hydroacoustic dataset acquired with a DigitalHyd TP-1 hydrophone within the NARLUGA system. We considered the following neural network architectures: multi-layer perceptron, long short-term memory, gated recurrent unit, and convolutional neural network. This paper provides a detailed description of the model architectures, the training parameters, and the preprocessing steps used to adapt the data representation to each type of model. On the simulated dataset, feature-based models (based on gammatone cepstral coefficients) achieved a test accuracy of above 99%, whereas recurrent models trained directly on long raw sequences did not converge under the training settings used here. On the real-world dataset, the best feature-based model reached a weighted test accuracy of approximately 85%. The results confirm the validity of deep-learning-based algorithms for passive hydroacoustic classification, and highlight the importance of feature representation for robust performance and practical deployment.
The rapid advancement of Generative Artificial Intelligence (GenAI) presents a dual-edged impact on cybersecurity and information security. While its capabilities drive innovation and efficiency across industries, the widespread integration of self-learning generative algorithms also introduces novel cyber threats. This paper explores the technical, operational, and strategic risks associated with GenAI, emphasizing its role in cyberattacks, data manipulation, and security vulnerabilities. Key concerns include data confidentiality breaches, misinformation proliferation, and the exploitation of AI for cybercrime, including phishing, deepfakes, and AI-generated malware. The study also evaluates threats to information integrity, highlighting AI-driven disinformation campaigns and adversarial attacks that compromise decision-making processes. Additionally, we discuss the regulatory challenges posed by GenAI, including legal ambiguities in data protection and intellectual property rights. Drawing from cybersecurity frameworks and real-world case studies, the paper proposes strategies for mitigating GenAI-related threats, including AI governance, ethical design principles, and enhanced cybersecurity protocols. By addressing these risks, policymakers, security professionals, and AI developers can work toward a balanced approach that maximizes AI’s potential while safeguarding digital infrastructure and societal trust.
The growing number of criminal investigations involving cryptocurrency-related offenses, mainly investment fraud or crypto scams, has led to an increased frequency of Law Enforcement Agencies requesting information from cryptocurrency exchanges. However, the data provided by these entities often varies significantly in terms of format, structure, and level of detail, which complicates efficient processing and analysis. This article proposes the development of an online tool designed to automatically process data received from various cryptocurrency exchanges. The tool aims to convert disparate datasets into a standardized and readable format, thereby enhancing the effectiveness of investigative procedures and improving the consistency and quality of data analysis in criminal cases. The paper outlines the core functional assumptions of the proposed solution, presents its system architecture, and discusses example use cases. A prototype implementation has been deployed and evaluated on sample datasets from five major cryptocurrency exchanges.
