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        <title>Frontiers in Computer Science | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science</link>
        <description>RSS Feed for Frontiers in Computer Science | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-21T04:55:19.775+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1927789</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1927789</link>
        <title><![CDATA[Designing embodied learning experiences in an extended reality geography simulation]]></title>
        <pubdate>2026-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kukhyeon Kim</author><author>Jeeheon Ryu</author>
        <description><![CDATA[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 (LXD) principles. Quantitative measures assessed changes in academic self-efficacy and achievement, while post-test instruments captured perceived attention and learning satisfaction. Semi-structured interviews with a subsample of participants provided qualitative insights into their embodied learning experiences, analyzed through a stepwise coding process. Participants showed statistically significant pre-to-post increases in both academic self-efficacy and achievement, alongside high levels of attention and satisfaction. Qualitative analysis identified specific affordances and constraints that participants associated with their engagement and performance; conceptual reorganization through physical action offered the most coherent interpretation of this pattern, although the proposed mechanism was not tested directly. The findings point to four design considerations for XR learning environments: direct manipulation, repeatable practice, learner-controlled scaffolded feedback, and ergonomically considerate interfaces. This study offers design guidance for XR-based educational applications, to be interpreted within the constraints of a single-group, single-session design.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1864426</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1864426</link>
        <title><![CDATA[A user-centric federated cloud architecture for intelligent resource monitoring and QoS/QoE optimization]]></title>
        <pubdate>2026-08-20T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Gopika Fattepurkar</author><author>Pankaj Chandre</author>
        <description><![CDATA[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 ensuring satisfactory Quality of Experience (QoE) for end users. Furthermore, most existing studies primarily focus on system-centric optimization and provide limited consideration of user-centric requirements and adaptive monitoring mechanisms. To address these limitations, this paper presents a comprehensive survey and conceptual framework for a user-centric federated cloud architecture integrating intelligent resource monitoring with QoS/QoE optimization. The study systematically analyzes federated cloud architectures, resource management strategies, monitoring techniques, security mechanisms, and QoS/QoE-aware approaches through taxonomy-based comparative analysis. Based on the identified research gaps, a multi-layered conceptual architecture is proposed consisting of User, User-Centric, Monitoring, Resource Management, Federation, and Cloud Provider layers. The proposed framework incorporates Fuzzy Inference System (FIS)-based QoE evaluation, adaptive resource allocation, intelligent monitoring, SLA tracking, and interoperability management to improve service reliability and user satisfaction. In addition, the paper highlights security, privacy, and healthcare-specific application challenges in federated cloud environments. The study concludes by identifying open research issues and future directions involving AI-driven optimization, blockchain-enabled trust management, and edge-cloud integration for next-generation federated cloud systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1844445</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1844445</link>
        <title><![CDATA[Toward practical migration to post-quantum SSH: system-level design and evaluation]]></title>
        <pubdate>2026-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shahid Allah Bakhsh</author><author>Inam ul Haq</author><author>Tarek Helmy</author><author>Fakhri Alam Khan</author><author>Shahid Latif</author><author>Jawad Ahmad</author><author>Muhammad Shahbaz Khan</author>
        <description><![CDATA[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-enabled OpenSSH and OpenSSL with Linux netem network emulation, automated experiment execution, SCP integrity verification, and statistical post-processing. The evaluation separates key exchange behavior from host key authentication. Specifically, it measures ML-KEM and hybrid ML-KEM as SSH key exchange mechanisms, and ML-DSA as a host-key signature mechanism. Experiments are conducted under controlled RTT and packet-loss conditions using a gateway virtualised client-server testbed. The results show that ML-KEM and hybrid ML-KEM can be integrated into SSH without prohibitive application-level session setup overhead in the evaluated environment. Among the evaluated configurations, ML-KEM-768 demonstrates comparatively lower SSH session establishment latency at 50 ms RTT with 0% packet loss. ML-DSA-44 achieves the lowest host-key authentication latency under the same conditions and maintains relatively stable performance at 150 ms RTT with 5% packet loss. SCP throughput results for 100 MB and 200 MB transfers indicate that sustained transfer performance is mainly influenced by RTT and transport-layer dynamics using a single dominant key exchange configuration. These findings support migration toward standardized post-quantum mechanisms in SSH-based gateway and remote-access environments, provided that algorithm choice and system configuration are validated under representative workloads and network conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1863891</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1863891</link>
        <title><![CDATA[Enhancing cybersecurity with Explainable Artificial Intelligence: technical framework and applications in training labs]]></title>
        <pubdate>2026-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmad Almufarreh</author><author>Ashfaq Ahmad</author><author>Muhammad Arshad</author><author>Choo Wou Onn</author><author>Yegon Michael Kiplangat</author>
        <description><![CDATA[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 purely visual add-on. Using the UNSW-NB15 benchmark, we compare three widely used classifiers—Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)—and then analyse the strongest performer (RF) with post-hoc explainability tools: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). RF achieved 95.3% accuracy (precision 94.8%, recall 96.1%, F1-score 95.4%), exceeding DT and SVM on the same split. LIME and SHAP consistently highlighted traffic-volume and duration-related features (e.g., destination bytes, source bytes, and flow duration) as influential drivers of intrusion predictions, providing actionable hypotheses for analyst triage and policy refinement. We further discuss how explanation outputs can be operationalized in cybersecurity training labs through auditable “rationale artifacts,” while clarifying that any observed reduction in false positives should be interpreted as the outcome of explanation-guided interventions (e.g., threshold tuning and triage rule adjustments) rather than a direct causal effect of generating explanations. Finally, we outline necessary research extensions—controlled baselines, robustness testing, and explanation stability/faithfulness analysis—to ensure reliable deployment of LIME/SHAP in safety-critical IDS settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1926740</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1926740</link>
        <title><![CDATA[Integrating human–computer interaction and real-time thermal imaging: user-centered design of a thermographic system for physical rehabilitation]]></title>
        <pubdate>2026-08-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Margarita Kaplun-Mucharrafille</author><author>Alberto Rossa-Sierra</author><author>Rita Q. Fuentes-Aguilar</author><author>David Vidaña-Zavala</author>
        <description><![CDATA[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-time thermal acquisition, processing, and analysis within a unified clinical workflow. The proposed system is based on a modular architecture that decouples radiometric processing from visual representation, enabling accurate temperature measurements while maintaining low-latency interaction. A deterministic data pipeline is implemented to support real-time visualization (~30 fps), region-based thermal analysis, and structured data persistence. The interface was developed using an iterative usability engineering approach, incorporating feedback from clinical specialists to optimize interaction, reduce cognitive load, and improve workflow efficiency. Device validation was performed using a controlled gray-body cavity as a reference source, ensuring traceability and measurement consistency under standardized conditions. Preliminary evaluations in a clinical-like environment demonstrate stable thermal acquisition, reliable measurement behavior, and improved usability during data capture, analysis, and reporting tasks. This work contributes a clinically-oriented thermographic system that combines validated measurement processes with a user-centered interface design, highlighting the importance of integrating computational architecture and human–computer interaction principles in medical device development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1897752</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1897752</link>
        <title><![CDATA[CT-EWS: graph-centric early warning for multi-protocol cyber campaigns using long-memory forecasting and explainable attribution]]></title>
        <pubdate>2026-08-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rakesh Kumar Sehgal</author><author>Rakesh Matam</author>
        <description><![CDATA[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, a cyber threat early warning system that treats adversarial activity as a dynamic multi-protocol campaign graph. Honeypot telemetry from heterogeneous protocols is aggregated centrally and fed into SOC-oriented workflows. Each interaction becomes a node, with edges drawn from temporal proximity, behavioral similarity, infrastructure overlap, and cross-protocol relationships. Community extraction over this graph reconstructs coordinated multi-stage campaigns and consolidates fragmented alerts into coherent campaigns. For campaign forecasting, we apply FARIMA-based long-memory modeling on the Largest Connected Component (LCC) of the campaign graph, gaining a lead time of 18–22 min over ARIMA baselines. SHAP-based attribution explains which behavioral signals contribute to each anomaly, with 91.4% rank-weighted feature relevance and an explanation fidelity of R2 = 0.88. The detected subgraphs are mapped to MITRE ATT&CK for ICS, covering 100% of tactics and 84% of techniques. On real Internet-facing honeypot traffic, CT-EWS achieves a Normalized Mutual Information (NMI) of 0.87 and an F1 score of 0.89 for campaign reconstruction, and stays stable under graph perturbations of up to 10%–20%. Together, relational graph modeling, long-memory forecasting, explainability, and ATT&CK-aligned context turn passive honeypot infrastructure into a proactive early warning capability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1860996</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1860996</link>
        <title><![CDATA[Agentic social affordance framework (ASAF): agent identity design as a collaboration interface in multi-agent systems]]></title>
        <pubdate>2026-08-17T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Meng-Han Lee</author>
        <description><![CDATA[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 AI systems. We propose that agent identity design functions not merely as a user interface convention, but as a collaboration interface—structuring how users perceive, approach, and engage with each agent, and thereby influencing the quality of Human-Agent collaboration outcomes. Specifically, ASAF adopts the analytical separability of the social affordance layer and the engineering orchestration layer as a framing assumption—an organizing distinction that structures design analysis—rather than as a testable claim about effect-independence. The layers’ downstream effects can and do interact; the framing assumption is useful because it identifies a class of design considerations (cognitive posture, role-schema activation, oversight calibration) that is irreducible to engineering choices alone. ASAF comprises three mechanisms: Identity Signaling, Behavioral Priming, and Collaborative Governance, and specifies their boundary conditions through a four-tier Identity Signal Fidelity Spectrum and an individual-difference moderating variable (anthropomorphizing vs. instrumentalizing cognitive style). We situate ASAF in relation to existing affordance theory, the CASA paradigm, and classical multi-agent systems research, identifying a directional reversal: where classical MAS used roles, norms, and coordination to constrain autonomous agents, ASAF applies the same organizational vocabulary to structure the cognition and oversight of human operators who remain in the loop. ASAF positions social affordance design as a first-class design responsibility that engineering orchestration cannot subsume, with concrete implications for multi-agent system design. We outline directions for future empirical validation, including a factorial design for characterizing the empirical interaction surface between the social affordance and engineering orchestration layers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1840175</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1840175</link>
        <title><![CDATA[Integrating explainable AI with generative models for IoT intrusion detection systems: a systematic review]]></title>
        <pubdate>2026-08-14T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Jameela A. Hassan</author><author>Manal Abdullah</author><author>Dania Aljeaid</author>
        <description><![CDATA[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 underexplored.MethodsWe conducted a PRISMA-guided systematic review of studies published from 2014 to 2025 and retrieved from IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect. Eligible studies were categorized as (i) generative augmentation pipelines, (ii) XAI-enhanced IDS models, or (iii) hybrid generative-explainable frameworks.ResultsTwenty-one studies were included. GANs and conditional GANs dominated generative IDS research. Approximately 39% of GAN-based and 52% of cGAN-based studies incorporated post-hoc XAI methods such as SHAP and LIME. None of the reviewed InfoGAN-based IoT IDS studies integrated formal XAI mechanisms.DiscussionExisting research remains fragmented, with gaps in interpretability, cross-dataset robustness, explanation stability, real-time scalability, and deployment readiness. Future work should develop transparent, robust, and efficient generative-XAI IDS frameworks for IoT security.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919566</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919566</link>
        <title><![CDATA[An automated explainable decision-support system for multi-criteria assessment of functionalized implant coatings]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karlygash Alibekkyzy</author><author>Madina Batalova</author><author>Paweł Komada</author><author>Saule Belginova</author><author>Madina Bazarova</author><author>Talshyn Keribayeva</author><author>Indira Karymsakova</author><author>Yergazy Zheksenov</author>
        <description><![CDATA[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 framework integrates data preprocessing, indicator normalization, weighted scoring, implementation-readiness assessment, sensitivity analysis, explainable classification, and visualization. Biological effectiveness, engineering and operational reliability, and manufacturability and scalability were used as the three evaluation domains. Published experimental data from 2015 to 2025 on titanium oxide nanostructures, hydrogel coatings, hybrid nanocomposites, and bioactive surface modifications were used for validation.ResultsThe framework generated reproducible domain scores, integral performance scores, implementation classes, and explainable rankings. TiO₂-Sr, Grad-Ti, and TiO₂-NT achieved the highest scores because of their balanced biological performance, engineering reliability, sterilization compatibility, and technological scalability. The ranking remained stable under variations in weighting factors.DiscussionThe proposed framework transforms heterogeneous biomedical data into a transparent and reproducible decision-support workflow. It can serve as the computational core of future biomedical material-selection platforms and support preliminary coating screening, technology-readiness evaluation, and data-driven engineering decisions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1893718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1893718</link>
        <title><![CDATA[System-level governance and evaluation of accessible digital learning systems in higher education: a systematic review]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Naeema Abdulrahman Alhasan</author>
        <description><![CDATA[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 governance and strategies for digital transformation. A systematic review method was designed to answer this question, using peer-reviewed studies identified through systematic database searching from the past ten years of research to explore the status of accessible digital learning across system-level governance. The literature was analysed using a thematic synthesis to identify patterns across accessibility governance and evaluation. Findings indicate that accessibility within higher education is prone towards a focus on compliance and technical issues rather than strategic integration, leading to fragmented governance responsibilities across higher-education institutes. Weak accountability and poor ownership held back progress while conversely leadership commitment enhanced implementation. Frameworks for evaluation were dominated by WCAG-based technical auditing at the expense of students and inclusion, whilst gaps in staff training and expertise exacerbated institutional resistance towards change.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892143</link>
        <title><![CDATA[Adaptive multi-domain threat detection in critical infrastructure via context-gated risk aggregation and ANOVA validation]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Geetanjali Rathee</author><author>Razi Iqbal</author><author>Hafiz Husnain Raza Sherazi</author><author>Fizza Azhar</author><author>Mubashir Ali</author>
        <description><![CDATA[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 areas, which may invite intruders to harm the system's performance. This article proposes a unified mechanism integrating a secure policy framework comprising a multi-risk security model, an ANOVA-based statistical validation mechanism, and a context-aware security policy to ensure resilient and secure critical infrastructure. The proposed mechanism captures heterogeneous risks across the physical, cyber, and operational domains using separate encoders that help in modeling characteristics of each domain. Furthermore, a context-gated attention mechanism is utilized to measure weights for temporal, behavioral, and environmental signals, which ensures threat protection without retraining. An ANOVA-based statistical validation is used for quantifying improvements in overall performance across multiple metrics, including F1, accuracy, and resiliency score. The proposed mechanism ensures optimal and accurate decision-making in a multi-domain environment by reducing vulnerabilities in critical infrastructure.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1859574</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1859574</link>
        <title><![CDATA[A feature-enhanced and attention-focused intelligent detection method for water-floating garbage]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhengwei Wang</author><author>Baijing Wu</author><author>Ke Gao</author>
        <description><![CDATA[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 occluded targets through multi-scale network filtering, thereby enhancing the extraction of subtle garbage features. Secondly, the Meta-ACON activation function is introduced to improve the robustness of the model in capturing the nonlinear characteristics of water-floating garbage. Next, a Context Aggregation attention mechanism is embedded into the neck to guide the network to focus more effectively on garbage target regions. Finally, the bounding box regression loss is replaced with Wise-IoU. By exploiting its dynamic non-monotonic focusing mechanism, the proposed model further improves detection accuracy and localization precision. Experimental results on the Yellow River water-floating garbage dataset demonstrate that, compared with the baseline model, MFPF-YOLOv11s improves precision, recall, and mAP50 by 3.1, 3.2, and 2.3%, respectively, with only 0.06 M additional parameters and 0.7G additional FLOPs. These results indicate that the proposed method can meet the requirements of high-precision water-floating garbage detection and provide technical support for the intelligent monitoring and management of aquatic environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1868904</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1868904</link>
        <title><![CDATA[Assessing Machine Learning understanding in High School Healthcare AI curriculum]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michael Cassidy</author><author>Jessica M. Karch</author><author>James K. L. Hammerman</author><author>Carlo Pinciroli</author><author>Beth Price</author><author>Brandy Jackson</author><author>Luk Hendrik</author><author>Kathryn Jessen Eller</author>
        <description><![CDATA[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 foundational ML concepts. This study developed and provided initial validation evidence for an assessment of foundational ML understanding while examining student learning within a context-based healthcare AI curriculum.MethodsWe conducted a multi-year study in which high school students engaged in an 18-lesson curriculum integrating data science, AI, and ML through authentic healthcare applications. Students analyzed real-world datasets, built and evaluated ML models, and participated in a collaborative datathon with healthcare and data science professionals. A curriculum-aligned assessment measuring foundational ML concepts was administered before and after instruction, and psychometric analyses, as well as multiple linear and multilevel regression models were used to evaluate assessment quality and changes in student learning.ResultsStudents entered the curriculum with limited but non-random knowledge of ML concepts and demonstrated statistically significant gains following instruction. The assessment also demonstrated improved reliability and item discrimination following instruction, suggesting that students developed more coherent conceptual knowledge of ML. At the same time, the findings identified several issues with the assessment and some of its items that warrant further refinement.ConclusionThis study provides initial validation evidence for one of the few assessments of foundational ML concepts developed for secondary students and provides evidence that a context-based healthcare curriculum can support foundational ML learning. The findings also highlight the importance of addressing both authentic contexts and continued assessment development to support the technical and societal dimensions of improving AI literacy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841675</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841675</link>
        <title><![CDATA[Comparative analysis of knitted resistive strain sensors in joint motion measurements]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alina Schepers</author><author>Yao Zhang</author><author>Yu Xiao</author>
        <description><![CDATA[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 plain weft-knitted resistive strain sensors made from conductive silver-coated yarn and Lycra-based elastic yarn across multiple patterns, and (2) evaluating three integration methods–hand-sewn attachment to finished garments, snap-button attachment, and direct in-garment knitting. We find that the plated knitting pattern with conductive material on the knit side and the elastic non-conductive yarn on the purl side achieves the best sensor characteristics, with a gauge factor of 14.616 during stretching and 13.300 during release. For joint angle estimation, we compare multi-layer perceptrons and a random forest under both static holds and continuous motion for all three integration methods. Performance degradation from resistance drift can be substantially mitigated through pre-processing, including linear detrending and predicting relative rather than absolute angles. However, models trained on one integration method show limited transferability to others, underscoring the need for method-specific calibration. This work provides practical guidance on the design, fabrication, integration and data processing of knitted strain sensors for joint motion measurement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1838774</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1838774</link>
        <title><![CDATA[Probing variables in QUBOs improves the probability of reaching the global minimum with the roof dual]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuelai Wang</author><author>Georg Hahn</author>
        <description><![CDATA[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 contribution, we focus on the roof dual, a guaranteed lower bound on the global minimum of a QUBO. Importantly, in certain cases, the roof dual computation allows one to reveal the solution yielding the global minimum in addition to providing a valid lower bound. In this study, we develop an algorithm to solve QUBOs based on probing (that is, the exhaustive enumeration of certain variables). In particular, we show that probing sets of variables increases the probability of reaching the global minimum with the roof dual, and we prove a theoretical result that allows one to check whether the global minimum was found during probing. Although the runtime of the proposed algorithm is exponential, our empirical studies show that it allows one to find the global minimum of both random QUBOs and QUBOs for the Maximum Clique problem faster than brute-force enumeration and certain other state-of-the-art algorithms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1864858</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1864858</link>
        <title><![CDATA[Modeling affective and autonomic user states in immersive de-escalation training: a multimodal study with police officers]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>John E. Muñoz</author><author>Jennifer Lavoie</author><author>Lisa M. Whittingham</author><author>Ifigeneia Mavridou</author>
        <description><![CDATA[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 measures, together with complementary experiential measures, could capture meaningful affective responses during a VR-based mental health crisis response (MHCR) training scenario.MethodsEleven trained active-duty police officers in Canada completed a co-designed VR interactive simulation involving a virtual individual experiencing symptoms of early psychosis with two endings (escalation and de-escalation) depending on user interaction with the virtual individual. Participants wore an HTC Vive Pro VR headset integrated with an Emteq Pro mask comprising multiple facial electromyography (fEMG) and an EmotiBit wearable. We performed event-related analyses that focused on key scenario moments: Start, Door, and Action/Resolution. The events were analyzed in terms of physiological metrics and affective ranges of valence and arousal using fEMG signals across seven facial muscle channels, heart rate, HRV metrics, electrodermal activity (EDA), and pre/post subjective measures of perceived usefulness and debriefing.ResultsSignificant event-related differences in facial activation, particularly for the orbicularis and zygomaticus channels, with stronger responses at the “Door” event relative to baseline. Frontalis and corrugator activity showed descriptive increases during escalation and more negative outcome pathways. Valence-arousal patterns indicated that officers who drew their weapons during the de-escalation scenario clustered more strongly in negative valence regions, whereas others showed broader, more adaptive affective trajectories. Heart rate was significantly higher during the scenario than baseline, and HRV-based comparisons suggested more adaptive autonomic profiles in officers that scored high in de-escalation skills. EDA generally increased during the scenario for most participants, indicating elevated sympathetic activation.DiscussionThese exploratory findings support the feasibility of using fEMG within immersive VR police training to capture fine-grained affective dynamics during de-escalation. The combination of event-related facial analysis, autonomic measures, and subjective evaluations provides a richer view of how officers respond to high-stakes mental health encounters.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873726</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873726</link>
        <title><![CDATA[Dissociating communicative success from representational alignment in common ground formation]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Taiki Yamamoto</author><author>Junya Morita</author><author>Ryuichiro Higashinaka</author><author>Yugo Takeuchi</author>
        <description><![CDATA[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 update the perceptual module with conditions that update the language generation module, while manipulating whether discriminative learning is applied to clarify differences among candidates. Symbolic alignment (referent-identification accuracy) and representational alignment—operationalized as feature-space similarity between sender- and receiver-generated images, our proxy for the alignment of internal representations—are used as evaluation metrics. When the language generation side is updated and discrimination is introduced, symbolic alignment improves substantially while representational alignment declines. In contrast, updating the perceptual module tends to preserve representational alignment. These results indicate that evaluating common ground requires multiple levels of metrics rather than a single indicator.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1885582</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1885582</link>
        <title><![CDATA[A traffic flow forecasting model with embedded sparse dynamic graph convolution and multi-head attention LSTM]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Guozheng Li</author><author>Baijing Wu</author><author>Ke Gao</author><author>Yaning Huang</author><author>Guanghui Yan</author>
        <description><![CDATA[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 Multi-Head Attention. First, a sparse dynamic graph convolution module is developed to integrate the physical topology and implicit semantic features of traffic flow data. Combined with a Top-K strategy, the adjacency matrix is dynamically reconstructed to enable adaptive capture of time-varying spatial dependencies while effectively suppressing long-tail noise. Next, an improved LSTM unit enhanced by multi-head attention is designed to model abrupt events and multimodal evolution patterns in traffic flow. Through multi-subspace projection and dynamic weight allocation, the hidden-state update process of the LSTM unit is restructured, thereby enabling deep extraction of traffic temporal features. Finally, a deeply coupled spatiotemporal feature network is constructed, in which graph convolution is further employed to refine the output states of the LSTM gates, achieving deep coupling and synchronized extraction of spatial and temporal features. Experimental results on the public PEMS04 and PEMS08 datasets demonstrate that the proposed model significantly improves forecasting accuracy, confirming that ESDG-ALSTM is more sensitive to abrupt events and multimodal evolution patterns and can effectively enhance prediction performance in complex traffic flow scenarios.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1806452</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1806452</link>
        <title><![CDATA[A priori acceptance of voice-based HMIs in adaptive cruise control]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tomoki Miyamoto</author><author>Ryoki Sato</author><author>Daisuke Katagami</author><author>Kazuhiro Fujikake</author><author>Takahiro Tanaka</author>
        <description><![CDATA[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 complementary modality; however, empirical evidence regarding their acceptability for ACC remains limited, particularly in partially automated systems that require continuous driver monitoring. This study examines the subjective a priori acceptance of voice-based ACC notification designs through a video-based experiment. Participants (N = 240) view standardized highway driving scenarios and evaluate multiple HMI conditions that vary in informational and cautionary audio–visual content relative to a silent baseline providing purely visual content. The results reveal significant differences in acceptance across notification conditions. Overall, simple notification strategies exhibit consistently moderate acceptance, without eliciting strong rejection, whereas more detailed or strongly directive voice messages tend to be evaluated less favorably. Furthermore, the exploratory analysis yields preliminary, hypothesis-generating insights into age-related patterns in user evaluations. Overall, our findings suggest that the design of voice notifications may be an important factor in shaping the a priori acceptance of ACC and emphasize the need for further research targeting diverse user groups.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848830</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848830</link>
        <title><![CDATA[STARS: extending interactive task learning with large language models]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>James R. Kirk</author><author>Robert E. Wray</author><author>John E. Laird</author>
        <description><![CDATA[Interactive Task Learning (ITL) enables cognitive agents to learn novel tasks (in one shot) from natural-language instruction and allows humans to customize agents to align with individual preferences. ITL relies on reasoning over and learning from multiple sources of knowledge, a strength of cognitive architectures. However, ITL requires frequent human input, which can be tedious and time-consuming. We evaluate large language models (LLMs) as an additional source of knowledge for ITL. We summarize initial experiments exploring the potential use of LLMs in ITL and then describe a novel method (STARS) that markedly improves the reliability of task learning from LLMs for embodied ITL agents. We demonstrate that LLMs speed agent learning and greatly reduce the human effort required to achieve robust, reliable, and repeatable task performance.]]></description>
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