Frontiers in Computer Science | New and Recent Articles

The accelerated pace of digitalization has intensified the need for robust privacy and data protection measures to mitigate economic and security risks. This study examines the current landscape of data protection legislative frameworks and organizational policies in Saudi Arabia. Using a convergent mixed-methods approach, the research combines a systematic thematic analysis of six prominent publ…

With the significant increase in demand for Internet-based resources, the number of threats to users’ security and privacy has also increased. Users face online money fraud, cyberbullying, cyberstalking, and online identity theft. Cybersecurity researchers are continually uncovering new threats and their mitigation strategies. In this paper, we present a qualitative study to identify technologica…

Security in Industrial Internet of Things (IIoT) networks faces structural limitations when threats are generated concurrently by distributed, heterogeneous nodes. Current systems, whether local or centralized, even when employing advanced artificial intelligence models, produce alerts whose overall validity cannot be verified. At the same time, existing blockchain solutions are primarily used as…

This paper delves into the transformative potential of artificial intelligence in revitalizing ethnomathematics within African educational systems, positioning this intersection as a key strategy for decolonizing mathematics education. Drawing on decolonial theory, ethnomathematical research, and emerging AI applications, it develops a comprehensive framework that addresses the persistent challen…

IntroductionIn recent years, smartphones have become crucial tools in the daily lives of adolescents, offering opportunities but also raising concerns regarding Problematic Smartphone Use (PSU). While intensive use is often assumed to drive dysfunction, the relationship between actual usage, perceived usage, and PSU remains debated. This study analyzes smartphone usage patterns among Italian high…

In recent years, architectural design has incorporated principles from neuroscience to improve human experience in built environments, giving rise to neuroarchitecture as an emerging discipline. Although numerous studies examine cognitive and emotional responses to architectural spaces, the state-of-the-art remains fragmented, particularly regarding how design features shape attention, memory, an…

BackgroundHeart rate variability (HRV) is a widely used digital biomarker reflecting autonomic regulation and has been associated with diverse cardiovascular, critical care, and stress-related outcomes. In parallel, AI and machine-learning methods have expanded rapidly in HRV-based prediction, often achieving strong predictive performance. However, clinical translation remains constrained by limi…

Next-generation communication systems demand uninterrupted mobility across heterogeneous access networks, where users regularly switch among technologies (e.g., Wi-Fi, 5G, and future 6G networks). Network-Based Distributed Mobility Management (NB-DMM) scales and routes better than centralized mobility solutions, but vertical handovers across heterogeneous access domains may still cause handover l…

IntroductionThIntrusion Detection Systems (IDS) for Internet of Things (IoT) and edge environments require datasets with unambiguous labels, yet existing datasets often mix benign and malicious traffic within the same capture window, producing ambiguous flow labels that may distort model evaluation.MethodsThis work introduces the TRUST Lab dataset, a flow-based traffic collection generated in an …

Federated learning (FL) is a decentralized machine learning (ML) approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for data sharing between IoT devices while fostering collaborative model improvement. Nonetheless, concerns arise from the …

Platform engineering has become the dominant approach to managing developer infrastructure at scale, with industry surveys indicating that 94% of organizations have adopted or plan to adopt dedicated platform teams. Despite this rapid practitioner uptake, academic research remains scarce: a systematic search across five major databases identified fewer than a dozen peer-reviewed papers from reput…

As Machine Learning Operations (MLOps) adoption accelerates, systematic integration of explainability is imperative for reliability, transparency, and continuous quality assurance. This paper presents a scoping review examining how explainability is integrated across the MLOps lifecycle, encompassing data handling, model development, and deployment. Each phase is further analyzed through its suba…

IntroductionRadio labeling of graphs extends the channel assignment problem by assigning non-negative integers to vertices of a connected graph G such that |h(℘)−h(𝓆)|≥diam(ℊ)+1−d(℘, 𝓆). The objective is to minimize the span, leading to the radio number rn(G).MethodsWe consider a class of outerplanar graphs with vertex set {u1, v1, x1, …, xn, y1, …, yn} and a structured edge set combining path an…

The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods aim to enhance end-users' trust in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel trust measure in XAI systems, allowing their r…

Real-time weapon detection in video surveillance is a critical capability for artificial intelligence assisted security systems, particularly in scenarios constrained by low latency, limited computational resources, and strict power efficiency requirements typical of edge artificial intelligence deployments. This work presents a comparative analysis of lightweight YOLO based object detectors, nam…

IntroductionDifferences in prior knowledge among incoming medical students pose a persistent challenge for universities. To promote more individualized and equitable preparation, a large language model-based learning platform is being developed at the University Medical Center Hamburg-Eppendorf. A central component of this platform is the automated generation of multiple-choice questions (MCQs) f…

IntroductionDrivers supervising Level 2 automation must maintain situation awareness while the system controls steering and speed. Miscalibrated trust can contribute to overreliance and lapses in monitoring, whereas insufficient trust leads to disuse. Prolonged supervision is associated with increased mind-wandering, which can slow reactions to critical events. This study tested whether brief edu…

Flipped classrooms require learners to actively regulate their learning processes, yet the relationships among self-regulated learning (SRL), engagement, social–emotional intelligence (SEI), and academic performance remain insufficiently integrated within learning analytics research. This study examined 96 third-year Computer Science and Engineering students enrolled in a semester-long Database M…