This systematic literature review examines the integration of natural language processing (NLP) in software requirements engineering (SRE) from 1991 to 2023. Focusing on the enhancement of software requirement processes through technological innovation, this study spans an extensive array of scholarly articles, conference papers, and key journal and conference reports, including data from Scopus, IEEE Xplore, ACM Digital Library, and Clarivate. Our methodology employs both quantitative bibliometric tools, like keyword trend analysis and thematic mapping, and qualitative content analysis to provide a robust synthesis of current trends and future directions. Reported findings underscore the essential roles of advanced computational techniques like machine learning, deep learning, and large language models in refining and automating SRE tasks. This review highlights the progressive adoption of these technologies in response to the increasing complexity of software systems, emphasizing their significant potential to enhance the accuracy and efficiency of requirement engineering practices while also pointing to the challenges of integrating artificial intelligence (AI) and NLP into existing SRE workflows. The systematic exploration of both historical contributions and emerging trends offers new insights into the dynamic interplay between technological advances and their practical applications in SRE.

Cybersecurity threats increasingly exploit cognitive heuristics, yet their structured role in security decision-making remains underexplored. This study examines how heuristic-driven behaviors influence vulnerability to cyberattacks, particularly in social engineering contexts. Using Exploratory Factor Analysis (EFA), followed by Confirmatory Factor Analysis (CFA), we identified two key cognitive dimensions: risk perception and compliance and security, shaping security decisions. Regression and mediation analyses revealed that risk awareness influences protective behaviors, but a security paradox persists—many users recognize risks yet fail to act accordingly. Clustering techniques further classified individuals into distinct cybersecurity profiles, highlighting variations in susceptibility. This research bridges cognitive psychology and cybersecurity, offering insights for designing more effective awareness programs and interventions. Understanding these cognitive vulnerabilities is essential for improving cybersecurity resilience and risk mitigation strategies.

Data has become a valuable resource due to the rapid growth of technology. Nevertheless, data has little value when held by its "collectors". The true value of data unfolds through its reuse, necessitating the role of data intermediaries to facilitate this process. From the data intermediation services, as employed in the Data Governance Act (DGA), we are going to discuss the data intermediaries organized in data cooperatives, which ensure the technical, legal, and logistical support for data transactions, according to the European framework. The aim of this paper is to analyze the role of the cooperative as a data-sharing intermediary within the European context. We will adopt a threefold approach, examining the topic from legal, psychological, and technical perspectives to achieve a comprehensive understanding. To accomplish our objectives, we will conduct a thorough literature review. The DGA does not specify whether data cooperatives are cooperative societies, whose primary function is to facilitate the reuse of data, or whether they are a form of cooperation between data subjects and one-person undertakings or SMEs (small and medium-sized enterprises), on the one hand, and potential data users, on the other. Therefore, we will consider both interpretations and make an effort to clarify the following: What type of organization is the data cooperative? What are the primary goals of the data cooperative as stated by DGA? What are the trigger issues and drawbacks associated with participating in a data cooperative from the legal and psychological perspective of a data subject and from the small entrepreneurs' point of view? Finally, we will briefly go over the technical solutions that will enable the data cooperative to operate as a cooperative society and, more broadly, as a data space across Europe.

Cultural practices, including reading, have evolved within digitally mediated environments, reshaping how digitally engaged Romanian book consumers interact with books. Notably, shifts in individual consumption and purchasing behavior now frame reading as a cultural practice influenced by social media platforms and other demographic and motivational factors. We investigated these dynamics through a cross-sectional online survey focusing on book purchasing and reading patterns, genre and format preferences, and the role of social networks. The analyzed data set was generated based on a non-probability sample of 230 digitally engaged Romanian book consumers. The interpretations were made based on descriptive and inferential statistical analyses obtained using the Python programming language (version 3.13.3). The results show a statistically significant difference between purchase and reading volume, although the practical magnitude was small-to-moderate, with a positive purchase-reading gap characterizing a substantial minority rather than the sample generally. Respondents also reported high engagement with fiction and frequent reading of printed books, which were measured as separate dimensions. Participation in digital reading communities was associated with higher reading activities and habits, although its relationship with book purchasing was comparatively weak. Respectively, the study provides valuable information regarding the adaptation of marketing strategies according to the behavior of modern Romanian book consumers, focusing on community-oriented recommendations and communication campaigns tailored to book consumers’ preferences and participation patterns.

Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs.