In an era marked by accelerated technological transformations, education is undergoing a process of metamorphosis that fundamentally redefines its current structure. The objectives of the present research are oriented toward analysing how participants in the educational process, teachers and learners, are facing new challenges and limitations. The article explores the relevant literature to identify the obstacles and inefficiencies of the educational process and examines how emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain can contribute to overcoming these barriers in the context of an ongoing technological revolution. The study aims to provide an in-depth understanding of how these technologies can support educational activities (teaching–learning–assessment) and the adaptation of education to ongoing transformations, thereby improving educational performance and shaping the future of the educational system.
The rapid evolution of recruitment processes has prompted organizations to explore advanced technologies that enhance efficiency, transparency, and fairness in talent acquisition. This review critically examines the integration of Blockchain and Machine Learning (ML) as transformative tools in addressing long-standing challenges such as credential fraud, skills mismatch, unconscious bias, and lack of process transparency. Blockchain provides a decentralized, immutable ledger for secure storage and verification of academic and professional credentials, thereby reducing fraudulent claims and streamlining verification through smart contracts. The convergence of these technologies creates robust frameworks where ML algorithms operate on authenticated data supplied by blockchain systems, reinforcing trust, accountability, and efficiency in recruitment workflows. This study highlights both the transformative opportunities and practical challenges of these technologies, providing insights for academia, practitioners, and policymakers to design sustainable recruitment strategies aligned with modern workforce demands.
Quantum Key Distribution (QKD) provides information-theoretic security based on quantum mechanics, but its practical deployment is limited by its reliance on classical infrastructure and inability to operate as a standalone solution. Recent research emphasizes hybrid approaches that combine QKD with post-quantum cryptography (PQC) to achieve practical and robust security. This paper presents a hybrid key distribution model integrating QKD with the post-quantum key encapsulation mechanism. The two independently generated keys are combined to derive a unified shared secret, enhancing resilience against both classical and quantum adversaries. The system is implemented using a simulated quantum environment, enabling the emulation of quantum communication over classical networks. The proposed approach is validated through a secure image transmission use case, demonstrating the correctness of the hybrid key exchange. The results highlight the feasibility of hybrid classical–quantum cryptographic systems as a practical step toward secure communication in emerging quantum network infrastructures.
Housing affordability remains a major concern for young Europeans, as growing real-estate prices and limited financial accessibility increasingly challenge their capacity to purchase or rent a home. This paper combines quantitative analysis with digital innovation to explore and visualize the determinants of housing affordability across EU member states. Using Eurostat and World Bank datasets, we perform statistical modeling to identify the key factors influencing young adults’ ability to access housing-such as income, mortgage availability, employment stability, and governmental support mechanisms. Beyond the empirical findings, we propose a mobile application with an interactive map designed to make the analysis more accessible to the public. The platform enables users to explore housing costs across Europe, visualize affordability differences through dynamic charts, and complete a short questionnaire that suggests EU countries best aligned with their income, openness to mobility, and occupational profile. This integrated approach bridges data science and decision support, offering both researchers and citizens a tool to better understand and navigate the European housing market.
This paper explores the paradigm shift in software engineering by positioning Artificial Intelligence (AI) as an active co-creator within the Software Development Life Cycle (SDLC). While traditional AI tools function as passive assistants, the proposed framework integrates large language models and predictive analytics to foster a collaborative environment between human developers and machine intelligence. Through a Retrieval-Augmented Generation (RAG) approach, the study evaluates improvements in requirements analysis, code generation, and proactive defect detection. Experimental simulations indicate a 57% reduction in lead time and a 37% decrease in defect density, highlighting significant productivity gains. However, the research also identifies the "Reviewer’s Paradox," where the cognitive load shifts toward verification and architectural oversight. The findings suggest that AI-human co-creation not only optimizes resource allocation but also necessitates a fundamental redefinition of developer roles in the era of AI-native software engineering.
Match-point prediction is an operationally relevant task in football analytics, supporting tactical preparation, squad management, and performance monitoring. Based on a dataset provided by InStat on the results of a struggling Romanian football team, this study proposes a reproducible and auditable decision-support workflow framed as a supervised binary classification problem that estimates the probability of securing at least one point by match end (90 minutes plus stoppage time) using match data. Predictors are engineered to represent three interpretable constructs: average team age, tactical deployment, and key-player exposure. The workflow was implemented in R using two intertwined frameworks for data processing and exploration (tidyverse) and Machine Learning (tidymodels). Five classification algorithms were benchmarked. Results provide some insights on the classification performance when applied to small datasets. Also, the average team age and key-player minutes emerge as the most important predictors in explaining the variability of point attainment for the reference team.
The purpose of this paper is to find the appropriate physical and behavioral biometric as well as contextual indicators for continuous authentication in a secure transactional application. The indicators must have good accuracy in identifying a user and be able to form the basis of an anomaly detector. In addition, their combination must maintain the usability of the application. Therefore, a compromise between accuracy and usability will be made so that the user can use the application easily and at the same time be protected from a potential thief. The paper relies on research and results from over twenty-five articles in the specialized literature to identify the best indicators, classifiers, and algorithms that have demonstrated good accuracy and usability. Furthermore, additional indicators specific to a banking application were considered. The identified indicators will be collected during the use of the application from several volunteer users and subsequently analyzed to obtain the results. To decide whether a user is legitimate, a score is calculated for the current session based on the indicators that have a weight based on the importance that was established following an opinion questionnaire. If the score falls below a certain value, then the current session is stopped immediately, and the person is logged out. The study was conducted on a banking application and among the indicators are the preference for displaying or hiding the balance, dominant hand for using the application, the way in which they return to the application, either through the PIN number or by using the fingerprint, the location and time from which the application is accessed, the amounts transacted, the way in which they press buttons. Overall, this research paper contributes to the literature by identifying and testing a unique set of indicators suitable for continuous authentication using behavioral biometric data in the context of a banking application.
As eKYC pipelines see increasingly more usage in verifying identities across banking, insurance and retail applications, bad actors are developing new ways of bypassing validation and gaining trusted access in protected environments. Deepfake detectors have their unique place in such verification pipelines and act as a defense against synthetic forgeries delivered through injection attacks. This paper evaluates the adversarial robustness of four architecturally diverse detectors under white-box and black-box attacks in a cross-domain setting. Under white-box conditions all four detectors are fully compromised, even at perturbation magnitudes that remain metrically imperceptible. Transfer attacks show that adversarial examples crafted against a single, freely available, model can evade other detectors with high reliability and query-based attacks achieve comparable results without any knowledge of model internals. These results indicate that evaluated deepfake detectors do not withstand adversarial manipulation under realistic attack conditions, raising practical concerns for production eKYC deployments and for compliance with the EU AI Act’s robustness requirement for high-risk biometric systems.
The fast changes in media technology fostered a shift from print to online newspapers, challenging digital journalism to address new issues such as usability and user experience. Because of the rapid expansion of online news websites, usability has not been a concern. Few studies exist that address the usability of online newspaper websites. The objective of this work is to analyze the usability of Romanian online newspapers. The task-based evaluation approach focused on typical tasks, aiming to identify typical usability problems. The evaluation results showed a messy organization of the webpage, disorganized placement of advertising, poor user guidance, prevalence of pages requiring intense scrolling, and increased cognitive load for the user. Online newspapers are designed with commercial interests in mind, and the use of advertising often neglects even the focus on delivering good quality news, not to mention a good reader experience. Usability is not a priority în Romanian online newspapers.
Geographic risk is a dimension of the essence in anti-money laundering (AML) and counter-terrorist financing (CFT) frameworks, yet most existing models treat country profiles as static and retrospective. Recent literature explores event-driven risk assessment and adverse media monitoring but lacks scalable, explainable systems tailored to compliance. This paper proposes a mixture-of-experts framework using large language models to assess ingested news content that is calibrated against structured geographic indices. The research explores whether deliberative multi-agent systems improve accuracy in multi-class geographic risk classification. Experimental results show that the framework outperforms zero-shot NLI baselines, achieving over 90% precision and recall post-calibration, especially in high-severity risk tiers. These findings support the integration of structured and unstructured data into dynamic compliance systems. The proposed architecture advances regulatory technology by introducing a modular, auditable, high-performance solution for real-time geographic risk monitoring, thereby bridging the gap between static indices and event-sensitive financial crime detection.
As quantum computers get closer to being used in real-life situations, existing methods to protect against cyber threats are becoming less effective. This paper presents a hybrid, quantum-enhanced intrusion detection system implemented as a Linux kernel module, integrating kernel-level monitoring, user-space intelligence, quantum machine learning (QML) and post-quantum cryptographic (PQC) signature verification. The system uses a zero-trust approach by requiring cryptographic authorization for legitimate actions while simultaneously detecting anomalous behavior. Final enforcement decisions are executed within the kernel, ensuring low-latency response and strong security guarantees. By decoupling heavy cryptographic operations from kernel space and leveraging quantum-enhanced analysis techniques, this approach achieves a balance between performance, scalability, and resilience against future quantum threats.
This paper presents a reference architecture for a learning center designed to support engineering education in television broadcast and media production. The proposed environment replicates a small-scale but operationally complete television broadcast facility and is intended to give students hands-on access to the equipment, software, and workflows that characterize modern newsrooms. The architecture is organized around four interconnected components that follow the content lifecycle: media acquisition, processing and planning, production, and distribution. Two original subsystems, previously designed and evaluated by the authors, are integrated into this architecture in order to expose students to current technological trends. The first is a cloud-based automated ingest subsystem, embedded within the media acquisition component, which removes manual steps from the file-transfer pipeline and accelerates the availability of mobile journalism media assets. The second is a supervised machine-learning subsystem for the automatic classification of Romanian news stories, embedded within the processing and planning component, which supports editorial organization and newsroom decision-making. Five learning scenarios are defined to illustrate how students engage with the architecture across all four components, ranging from mobile journalism capture and cloud-based ingest to control room operation and on-air delivery through a streaming platform. The paper’s contribution is the consolidated reference architecture and the integration of these two previously evaluated subsystems into a coherent educational framework. A systematic assessment of student learning outcomes is planned for future work.
Synthetic databases are increasingly used in research and industry to support testing, training, and analysis without exposing sensitive information. This paper proposes a practical framework for generating and validating synthetic databases, structured around a pipeline that ensures structural consistency, business relevance, and reproducibility. The framework is illustrated through a case study on freight transport in Romania, where a relational model was designed to capture entities such as clients, trains, conductors, and transported goods. A Python-based generator was developed to populate the database with realistic values under domain-specific constraints (e.g., valid national identifiers, capacity limits, distinct departure/arrival stations). Validation is focused on structural integrity, query performance, and privacy preservation. The results show that the generated dataset is both realistic and safe for academic or enterprise use, while the methodology is transferable to other economic and business contexts.
In the digital era, words have a significant influence on how cyber threats are defined and perceived. This article examines the impact of framing phishing and other cybercrimes as „cyberattacks” on user responses and the legal and security mechanisms triggered. Confusing these concepts may discourage reporting incidents to authorities, leading users to delete messages and destroy evidence, which allows criminals to continue undisturbed. The study emphasizes the need for a clear distinction between cyberattacks, which threaten national security, and common cybercrimes, to ensure appropriate responses and effective protection measures. Moreover, this article seeks to clarify the essential differences between cyberattacks and cybercrime, highlighting their legal and strategic implications. Through a comparative analysis and a review of recent legislation, the study underscores the challenges and opportunities in managing these complex and dynamic threats.
As quantum computing progresses from theory to practical systems, its environmental and economic sustainability remains underexplored. This paper analyzes both the carbon footprint and cost implications of quantum computing technologies, with attention to their alignment with global sustainability goals. Key contributors to energy consumption, emissions, and operational costs are identified across the quantum computing lifecycle: hardware manufacturing, cryogenic cooling, runtime power demands, and quantum circuit simulation. The study employs a qualitative methodology, integrating a comprehensive review of scientific literature, environmental assessments, cost analysis reports, and benchmarking frameworks. It further presents a comparative analysis of quantum and classical high-performance computing (HPC), evaluating energy efficiency, environmental impact, and cost-effectiveness across realistic scenarios. By addressing both sustainability and economic dimensions, this research provides new insights for developers and policymakers, supporting the advancement of greener and more cost-effective quantum technologies.
This paper examines the psychological underpinnings of human susceptibility to social engineering, with a focus on identifying and analysing the cognitive, emotional, and behavioural components that contribute to target vulnerability. Integrating insights from cybersecurity studies and psychological theory, this research seeks to construct a nuanced understanding of how threat actors manipulate human factors to bypass security protocols. Through critical analysis of empirical studies, documented attack scenarios, and profiling literature, the study aims to determine the strongest factors that influence one’s susceptibility to becoming a victim, as well as understand the process behind a threat actor. The paper defines multiple categories of users that are deeply correlated to social engineering attacks and aims to establish an early methodology of determining one’s typology.
Neural networks have recently found widespread application across various domains within IT software infrastructure. This study focuses on specific neural network architectures incorporating Long Short-Term Memory (LSTM) layers and their variations, aiming to effectively capture the dynamic, nonlinear nature of traffic data. LSTM networks are well-suited for learning long-term dependencies in sequential data, making them particularly effective for time series prediction tasks. To evaluate the performance of different LSTM-based architectures, we utilize a dataset comprising bus logs from New York City collected over a four-month period. The experimental results indicate that LSTM architectures demonstrate strong predictive capabilities and are well-suited for modeling complex temporal patterns in traffic data.
This study explores the convergence of Internet of Things and Digital Product Passport technologies as a catalyst for achieving a Smart Digital Circular Economy. Anchored in a systematic literature review, the research identifies key IoT-driven use cases that enhance sustainability practices within Circular Economy context. The study also proposes a conceptual framework for integrating IoT data, interoperability standards, and circular metrics into DPP systems. By transforming static product information into dynamic, real-time data, the study demonstrates how IoT–DPP integration enables circular value creation, resource efficiency, and digital transparency across all stages of the product lifecycle. The findings offer both theoretical insights and practical guidance for industry stakeholders, policymakers, and researchers seeking to implement the digital transformation of circular economy practices and the circular transformation of the digital economy.
Today's business environments are characterized by increasing complexity and volatility, which highlights the limitations of traditional process engineering models. Faced with nonlinear and unpredictable organizational phenomena, classical methods of analysis and optimization become insufficient, making it necessary to integrate more advanced theoretical and technological frameworks. Chaos theory provides a conceptual tool for describing these dynamics, and artificial intelligence brings the ability to identify hidden patterns and generate robust predictions in seemingly unstable systems. This paper examines the synergy between chaos theory and artificial intelligence in business process engineering, focusing on how machine learning algorithms and neural networks can support managerial decisions, optimize processes, and strengthen organizational resilience. The main contribution is the formulation of a conceptual and methodological framework through which chaotic models, augmented with artificial intelligence, can support the digital transformation of organizations. Although the integration of these paradigms raises challenges related to complexity, transparency, and data access, the research findings highlight the real potential for building adaptive, intelligent, and future-oriented business systems.
The paper's purpose is to describe a modular component of a complex system capable of generating didactic assessment test items with a pronounced adaptive character. The respective component has a potential for integration into various Learning Management Systems (LMS) platforms, such as Moodle, and could be exploited by a wide range of users: from teachers and students enrolled in various forms and/or levels of education to LMS platform administrators and LMS systems’ developers, accessible in open-source and/or commercial formats. From a technical perspective, the component represents a classifier that turns out to be a fragment of a program product that interacts with the database, but especially with the LMS system question bank, created by the course developer, in order to satisfy the functionalities of the software product in question. The developed classification module was designed to operate based on labels created through artificial intelligence (AI) and machine learning (ML). Thanks to the aspects specified at the implementation level, AI and ML, these bring the respective classifier into the category of supervised learning program products. Aligning with national and international trends regarding the educational system's digitalization, this paper falls into fields such as applied computer science, software engineering, and artificial intelligence.
