Frontiers in Computer Science | New and Recent Articles

IntroductionWith increasing global demands for transparency, disclosure labels are becoming a standard feature of e-commerce interfaces, and a central open question is how the presence of such a label, and what it discloses, changes consumer responses relative to no label at all. This study examines this question using AI-generated content as a focal, currently topical case of disclosed label con…

Cloud federation has emerged as an effective paradigm for integrating heterogeneous cloud infrastructures to achieve scalability, interoperability, and efficient resource utilization. However, existing federated cloud systems still face significant challenges related to dynamic resource management, intelligent monitoring, interoperability, and maintaining consistent Quality of Service (QoS) while…

Cyberattacks are growing in complexity, and machine-learning-based intrusion detection systems (IDS) are increasingly adopted to support scalable threat monitoring. However, high-performing models can be operationally difficult to deploy when their decisions are not interpretable or auditable. This paper studies explainability as a decision-support component in an IDS workflow rather than as a pu…

This study investigates the critical factors influencing learning experiences within Extended Reality (XR) simulation environments, bridging the gap between advanced technology and pedagogical practice. Utilizing a mixed-methods approach, a single-group pretest–posttest study was conducted with undergraduate students using a geography-themed XR simulation grounded in Learning Experience Design (L…

The migration of remote-access and industrial communication systems from classical public-key cryptography to post-quantum cryptography (PQC) requires careful evaluation at both the protocol and system levels. This paper presents PQC-E2E-CA, a system-level evaluation framework for reviewing post-quantum and hybrid cryptographic configurations in Secure Shell (SSH). The framework integrates OQS-en…

The growing exposure of IoT devices and industrial control systems (ICS) to the public Internet has significantly increased the attack surface. Most of the current systems still detect these threats only in retrospect. Honeypots are widely used to collect attack data, but they tend to surface only isolated alerts rather than the coordinated campaigns behind them. In this paper, we present CT-EWS,…

Infrared thermography has emerged as a non-invasive technique for monitoring physiological processes in clinical rehabilitation; however, its adoption remains limited due to challenges in usability, data integration, and measurement reliability. This work presents the design and development of a thermographic medical device with a user-centered graphical user interface (GUI), integrating real-tim…

As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration. This paper introduces the Agentic Social Affordance Framework (ASAF), a theoretical framework that extends Social Affordance theory into the context of multi-agent …

IntroductionThe rapid expansion of the Internet of Things (IoT) has increased cybersecurity exposure and highlighted limitations of intrusion detection systems (IDS), including class imbalance and inadequate representation of minority attack types. Generative models address data limitations, while explainable artificial intelligence (XAI) improves transparency; however, their joint use remains un…

IntroductionThe selection of functionalized implant coatings is a complex multi-criteria decision-making problem because biological, engineering, technological, and implementation-readiness indicators are heterogeneous and are frequently evaluated manually. This study proposes an automated and explainable computational decision-support framework for coating assessment and ranking.MethodsThe frame…

IntroductionArtificial intelligence (AI) and machine learning (ML) increasingly shape decision-making in high-impact domains such as healthcare, yet most secondary students lack opportunities to understand how these systems function or reproduce inequities. Advancing AI literacy in K–12 education requires context-rich curricula paired with valid assessments that measure students’ understanding of…

This paper presents a systematic literature review, exploring how governance and evaluation frameworks for more accessible digital learning in higher education have been conceived, put into action, and evaluated. The study is put forward in response to concerns about the accessibility of digital learning and perceived limitations regarding the integration of accessibility into institutional gover…

Critical infrastructure forms the backbone of modern society by providing an efficient and interconnected communication network. In addition to the emergence of various secure solutions, numerous other critical issues require resolution alongside the assurance of network security. Although complex security structures enhance security, they also introduce delays in storage, computation, and other …

To address the problems of low detection accuracy and large localization errors for small water-floating garbage targets under stacked occlusion, water surface fluctuations, and illumination variation, this study proposes a small-target water-floating garbage detection model named MFPF-YOLOv11s. Firstly, a multi-stage dilated residual module is designed to suppress interfering features caused by …

E-textiles, particularly knitted resistive strain sensors, have been proposed for joint motion measurements. Although combining non-elastic conductive yarns with elastic non-conductive yarns improves sensor performance, the effects of knitting patterns and strategies for integrating sensors into garments remain underexplored. This paper addresses both by: (1) comparing the characteristics of plai…

IntroductionMental health crisis response requires police officers to regulate their emotional and physiological states while making rapid decisions in socially complex situations. Virtual reality (VR) offers a promising platform for immersive de-escalation training, yet the integration of affective sensing into these environments remains limited. This study examined whether physiological measure…

Quadratic unconstrained binary optimization (QUBO) problems have attracted considerable attention in recent years due to advances in quantum annealing technology, which is focused on solving them. Solving QUBO problems involves finding the global minimum of their objective function. Several classical preprocessing and solving approaches have been proposed in the literature, and in this contributi…

In human–Large Language Model (LLM) interaction, communicative success is often used as a proxy for shared understanding. However, this assumption can also be examined in controlled agent–agent models that allow internal representations to be directly measured. We investigate whether increases in communication success guarantee alignment of internal representations by comparing conditions that up…

To address the limitations of graph convolution-based traffic flow forecasting models, in which predefined static graphs are unable to adapt to the dynamic evolution of road networks and conventional LSTM models fail to finely characterize temporal dynamics, this study proposes an ESDG-ALSTM traffic flow forecasting model, namely an Embedded Sparse Dynamic Graph Convolutional LSTM model with Mult…

Adaptive cruise control (ACC) is becoming increasingly common; however, many users still struggle to understand system states and interventions, which may hinder trust and acceptance. Prior research has focused primarily on visual human–machine interfaces (HMIs), but driving is a visually demanding task. Therefore, voice-based HMIs are increasingly being considered as an alternative or complement…