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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>
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        <pubDate>2026-05-30T06:47:46.455+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1776094</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1776094</link>
        <title><![CDATA[Trust based mobile charging robot selection in wireless sensor networks]]></title>
        <pubdate>2026-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdullah Aref</author><author>Eman Omar</author><author>Osama I. Aloqaily</author><author>Osama Al-Haj Hassan</author>
        <description><![CDATA[Energy depletion in battery powered Wireless Sensor Networks (WSNs) causes coverage gaps and can eventually lead to the complete failure of the network. Mobile charging robots (MCRs) can be used to replenish energy and maintain the continuity of the network. Most existing work in this area assumes that MCRs are reliable in their operations; however, in real-world scenarios, robots may malfunction or deliver incomplete charges. We propose a trust-based MCR selection mechanism that allows sensors to evaluate and select charging robots on the basis of their performance in past interactions. Trust computation is done locally at each sensor with minimal overhead and does not require centralized coordination. Simulation results show that trust-based selection can outperform random selection in terms of network coverage across different network sizes, battery capacities, and robot populations without increasing disconnection time by a large margin.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780565</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780565</link>
        <title><![CDATA[The anomaly of Chinese AI news anchors: a study of speech irregularities and their impact on news communication effectiveness]]></title>
        <pubdate>2026-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiaxian Li</author><author>Xing Pang</author>
        <description><![CDATA[IntroductionAlthough AI virtual news anchors have gained attention for their accurate and uninterrupted broadcasting, existing research has mainly focused on technical efficiency, leaving insufficient understanding of normative deviations in their speech and the impact on audience experience.MethodsThis study conducted in depth interviews with 11 Chinese news consumers and 2 technology practitioners from the Chinese state-media sector to examine audience perceptions of Chinese AI virtual anchor performance within the state-media context.ResultsResults showed that participants widely perceived deficiencies in sentence stress, intonation, and rhythm. These perceived normative deviations were reported by participants as reducing clarity, emotional resonance, and audience engagement. Beyond technical dissatisfaction, 9 out of 11 participants expressed value rational concerns, focusing on lack of human connection, aesthetic quality, and weakened social ritual functions of news broadcasting.DiscussionBased on these findings, the study proposes a working model in which perceived linguistic deviations are linked to experienced communication failure and an inferred sense of value imbalance. The findings suggest that sustainable development of virtual anchor technology may benefit from recalibrating the relationship between instrumental and value rationality, treating technical efficiency as a means rather than an end. These findings offer empirically grounded directions for human machine communication research and for exploring how humanistic values might be integrated into AI mediated news practice.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1821417</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1821417</link>
        <title><![CDATA[Machine learning-based malicious URL detection using feature selection techniques and WHOIS features]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lokesh Khedekar</author><author>Suvarna Pawar</author>
        <description><![CDATA[In today's cybersecurity landscape, malicious Uniform Resource Locators (URLs) continue to pose a serious threat, as they can be used to deliver malware, phishing, and unauthorized data access, all of which can result in significant financial and reputational losses. Innovative, data-driven methods must be developed because traditional detection methods, such as blacklisting and rule-based detection, cannot detect newly created, obfuscated, and temporary URLs. We aimed to create a consistent method for detecting malicious URLs by analyzing both the characteristics of the URL and the information collected from the WHOIS database for that URL. We utilized five different feature selection methods to identify the best features from 5,000 URLs (malicious or benign) so we could test how they would classify using five different types of machine learning (ML) classifiers: random forest, logistic regression, support vector machine (SVM), naive bayes, and k-nearest neighbor (KNN). The classification methods were Random Forest Feature Importance, Chi-squared, mutual information (MI), L1-lasso, and recursive feature elimination (RFE). The feature selection method that produced the best results for KNN classification was RFE, achieving an F1 Score of 0.988, an accuracy rate of 0.988, and an area under the curve (AUC) of 0.996. We also examined how the inclusion of WHOIS attributes (i.e., domain age, registration date, and privacy) affected the classifiers' ability to perform correct classification. From the experimental results, we see a significant improvement in accuracy, precision, recall, and F1-measure when we include features extracted from WHOIS data. Therefore, WHOIS domain registration details are highly significant when distinguishing between legitimate and malicious websites. Furthermore, we did an extensive exploration of 25 distinct combinations of feature selection methods and ML models. The proposed methodology is a safe, efficient, and interpretable way of detecting malicious domains. Cybersecurity practitioners can design more effective prevention models by leveraging insights from our research (e.g., on model selection, feature importance, and the utility of WHOIS attributes), thereby setting the stage for future research in ML-based cybersecurity methodologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1846770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1846770</link>
        <title><![CDATA[The predictive power of perceived digital competence on electronic assessment anxiety among preservice teachers]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yasemin Kuzu</author><author>Nur Sultan Uğur</author><author>Okan Kuzu</author>
        <description><![CDATA[IntroductionThis study investigated the predictive power of preservice teachers’ perceived digital competence on their electronic assessment anxiety.MethodsThe study employed a correlational survey design with preservice teachers enrolled in the Faculty of Education at a state university in Türkiye during the 2024–2025 academic year. Although 427 students initially participated, analyses were conducted with a final sample of 412 participants after preliminary screening for missing data and outliers. Data were collected using the Teacher Candidate Digital Proficiency Perception Scale and the Electronic Assessment Anxiety Scale. Descriptive statistics, Pearson product–moment correlation analysis, and stepwise regression analysis were performed.ResultsPreservice teachers reported a high level of perceived digital competence (M = 105.79, SD = 12.31) and a moderate level of electronic assessment anxiety (M = 62.23, SD = 12.45). Overall perceived digital competence was positively, weakly, and significantly related to electronic assessment anxiety (r = 0.282, p < 0.01). Among the sub-dimensions, computing competencies showed the strongest association with electronic assessment anxiety and emerged as the only significant positive predictor in the regression model (β = 0.320, R2 = 0.102, F = 46.699, p < 0.001).DiscussionThe findings suggest that perceived digital competence may not function as a uniform protective factor in digitally mediated assessment contexts. Greater perceived competence, particularly in computing-related skills, may coexist with higher anxiety in e-assessment settings. Teacher education programs should therefore address not only digital skill development but also the affective demands of electronically mediated assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1832169</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1832169</link>
        <title><![CDATA[A structured Kano-AHP framework for AI-assisted generative media production: experimental evidence from short-form animation design]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiayi Wang</author><author>Hyunsuk Kim</author><author>Nan Wang</author><author>Junfeng Peng</author>
        <description><![CDATA[Although generative AI has accelerated parts of media production, the quality of final outputs still depends on how clearly requirements are defined, prioritized, and maintained across prompting, revision, and selection. This study evaluates a structured workflow that integrates the Kano model and the Analytic Hierarchy Process (AHP) into the Double Diamond process for AI-assisted media production. Twenty practitioners each produced one anime-style vertical clip under either a baseline workflow or a structured package workflow, yielding 20 final clips (10 per condition). Five experts rated all clips on narrative coherence, visual style consistency, emotional expressiveness, motion smoothness, and technical quality. In a randomized partial-viewing design, 60 audience viewers each rated eight clips, giving 24 audience evaluations per clip. Conditions were compared using descriptive statistics, Hedges’ g, intraclass correlation coefficients, and linear mixed-effects models with Holm adjustment. Across all five dimensions, the package condition received higher ratings than the baseline condition in both expert and audience evaluations. Audience effect sizes ranged from g = 0.74 to 0.99, expert effect sizes from g = 0.98 to 1.32, and expert inter-rater reliability ranged from ICC(2,k) = 0.70 to 0.86. These findings suggest that, under a common toolchain, explicit requirement structuring and criterion weighting can improve the judged quality of AI-generated media outputs. The current design, however, evaluates the structured package as a whole rather than the separate effects of Kano and AHP.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1869722</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1869722</link>
        <title><![CDATA[Editorial: From memes to movements: how affordances shape resistance and collective action on TikTok]]></title>
        <pubdate>2026-05-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Lara Kobilke</author><author>Florian Primig</author><author>Christian Pipal</author><author>Pilar Lacasa</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1831250</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1831250</link>
        <title><![CDATA[AI-assisted MCQ creation increases item-writing flaws through automation bias: evaluation of a workflow of multiple AI agents with teachers]]></title>
        <pubdate>2026-05-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dennis Menze</author><author>Slaviša Radović</author><author>Niels Seidel</author>
        <description><![CDATA[IntroductionArtificial intelligence (AI) holds promising potential for generating educational resources such as personalized assessments. Despite this appeal, the impact of AI-assisted multiple-choice question (MCQ) authoring on item quality and on teachers' editing behavior remains insufficiently studied.MethodsThis study (N = 152 items from 19 teachers) compares MCQ quality across three conditions—teacher-only (T), AI-only (AI), and teacher–AI collaboration (T-AI)—using a 19-criterion item-writing-flaw (IWF) rubric and logged authoring-interaction density.ResultsNeither human raters nor AI models reliably differentiated item provenance, indicating that LLM-generated MCQs have reached a level of surface quality largely indistinguishable from human-authored material. Yet both AI and T-AI items carried significantly more flaws than T items, with the T-AI condition showing a marked increase (d = 1.06) consistent with an automation-bias pattern: teachers accepted AI drafts with minimal critical engagement, as evidenced by significantly lower interaction density in the AI-supported condition. Criterion-level analysis revealed a complementary pattern: AI items exhibited specific cueing biases—notably lexical overlap between stem and correct answer—but simultaneously avoided structural format flaws (e.g., true/false questions) that teachers commonly produced.DiscussionThese results underscore that the human–AI interaction workflow, rather than AI capability per se, is the critical quality determinant, and that AI-assisted assessment creation requires structured quality review processes that leverage AI's structural consistency while correcting its cueing biases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1815716</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1815716</link>
        <title><![CDATA[Navigating the data protection landscape in Saudi Arabia: policy effectiveness, barriers, and a strategic roadmap]]></title>
        <pubdate>2026-05-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alia Mohammed AlSulaimi</author>
        <description><![CDATA[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 public portals with a quantitative survey involving 200 professionals. The findings reveal that while Saudi organizations have established foundational policy frameworks, significant gaps persist in procedural clarity, contact channel disclosure, and third-party risk management. Statistical validation through Chi-square (χ2) tests highlights a critical ‘experience gap’ and systemic barriers, specifically regarding regulatory inconsistencies (χ2 = 11.6, p = 0.003) and Technological Limitations (χ2 = 14.8, p = 0.001). Based on these findings, the study proposes a six-pillar strategic roadmap designed to enhance regulatory compliance and foster a secure digital trust ecosystem in alignment with Saudi Vision 2030.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1865390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1865390</link>
        <title><![CDATA[Correction: Beyond the looking glass: multimodal LLM-based depth-sensing for spatial behavior modeling in media architecture]]></title>
        <pubdate>2026-05-20T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Zhikun Wu</author><author>Ava Fatah gen. Schieck</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1800175</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1800175</link>
        <title><![CDATA[Distributed verification of security events in IIoT networks using low-latency blockchain consensus assisted by artificial intelligence]]></title>
        <pubdate>2026-05-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Iván Ortiz-Gárces</author><author>Pablo Palacios</author><author>Javier Guaña-Moya</author><author>William Villegas-Ch</author>
        <description><![CDATA[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 passive logging mechanisms, introducing latencies incompatible with industrial requirements. This work addresses this gap through a distributed security event verification architecture that integrates AI-based detection with a blockchain consensus mechanism explicitly optimized for low latency. The AI models generate events enriched with a continuous suspicion score, which are evaluated by a set of independent validators and resolved using Byzantine-fault-tolerant, permissioned consensus. The experimental evaluation is performed using widely adopted open IIoT datasets, supplemented by a dataset designed for model validation and consensus processes under controlled load and disagreement conditions. The results show that consensus maintains average latencies below 100 ms and within the 95th percentile range under operational loads, with validation rates exceeding 700 events per second before saturation. Additionally, the event acceptance rate remains stable despite increased workloads, demonstrating that consensus does not amplify detection errors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780315</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780315</link>
        <title><![CDATA[Cybersecurity knowledge, perspectives, and challenges: insights from a diverse focus group]]></title>
        <pubdate>2026-05-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kiranbir Kaur</author><author>Kuljit Kaur Chahal</author>
        <description><![CDATA[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 technological challenges, their perspectives, and factors that act as barriers to cyberspace adoption by users of different age groups in India. Five focus group discussion sessions were held with children (senior secondary school students), graduates (university students), postgraduates (university students), senior citizens (older adults), and social activists (mixed-age group). Cybersecurity knowledge, awareness, and perceptions among diverse groups were discussed using a semi-structured question guide. Discussions were documented through a detailed manual note-taking process. Then, the notes were coded and analyzed thematically using an inductive coding process. Some key themes that emerged included diverse internet use, security and privacy concerns, dominance of social media, fear and avoidance, technology illiteracy, challenges with online transactions, and so on. After analyzing, it is clear that participants are not very aware of the cybersecurity and privacy consequences of their activities in cyberspace. Future initiators should focus on integrating cybersecurity education, awareness programs, and promoting a cyber-safe culture among diverse age groups of users. Thus, this research has implications for cybersecurity researchers, educationists, and policymakers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855557</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855557</link>
        <title><![CDATA[Decolonizing mathematical futures: the role of artificial intelligence in reviving and repositioning ethnomathematics in curriculum design in Africa]]></title>
        <pubdate>2026-05-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Terungwa James Age</author><author>Masilo France Machaba</author>
        <description><![CDATA[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 challenges of integrating indigenous mathematical knowledge into formal curricula. The analysis reveals that AI technologies offer unprecedented capabilities for documenting endangered mathematical practices, creating semantic bridges between indigenous and formal mathematical systems, and delivering culturally responsive personalized learning experiences that honor epistemological diversity. The four-component model proposed, comprising community-centered documentation, cultural-mathematical mapping, contextual curriculum development, and reflective implementation, provides a structured yet adaptable approach for developing AI-enhanced ethnomathematical curricula that center African mathematical heritage while preparing students for global engagement. However, the paper identifies critical concerns regarding technological colonialism, data sovereignty, and the potential for the essentialization of dynamic cultural practices, which must be addressed through participatory design processes and community governance. This research contributes to both theoretical and practical dimensions of educational transformation in Africa by demonstrating how emerging technologies, when implemented through decolonial frameworks, can support epistemological pluralism in mathematics education. The findings have significant implications for educational policy, teacher preparation, and technological development across the Global South, offering a pathway toward mathematics education that validates indigenous knowledge systems while equipping students with mathematical competencies for an increasingly digital world.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1773348</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1773348</link>
        <title><![CDATA[Beyond screen time: analyzing the discrepancy between objective and perceived smartphone usage in adolescents]]></title>
        <pubdate>2026-05-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elisa Saraceni</author><author>Lucia Migliorelli</author><author>Emanuele Frontoni</author><author>Lorenzo Stacchio</author>
        <description><![CDATA[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 school students to investigate the discrepancy between subjective perception and objective behavior.MethodsThe research involved 73 students and integrated self-reported data with objective logs collected via Digital Wellbeing (Android) and Screen Time (iOS), monitoring notifications, usage time, and screen unlocks.ResultsThe analysis highlighted significant discrepancies: students consistently underestimated the number of notifications received while overestimating their usage time. Crucially, linear regression analysis revealed that objective screen time and the age of first smartphone acquisition were not significantly associated with SAS-SV scores. Conversely, perceived usage time showed a significant positive association with PSU levels.DiscussionThe findings suggest that educational and parental interventions should shift from a prohibitionist approach based on time restrictions to a metacognitive approach aimed at improving digital awareness and self-regulation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1819538</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1819538</link>
        <title><![CDATA[Neuroscience-informed approaches in architectural design: a systematic review of cognitive measures and interactive technologies from an HCI perspective]]></title>
        <pubdate>2026-05-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ana Gabriela Núñez Ávila</author><author>Luis Miguel Quizhpi Campoverde</author><author>Kelly Yuzabeth Macas Aguilar</author><author>Andrea Jackeline Maza Parapi</author><author>Andrea Paulina Rodriguez Zúñiga</author><author>Priscila Cedillo</author>
        <description><![CDATA[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, and spatial orientation. This systematic literature review, conducted in accordance with established systematic literature review guidelines, investigates which neuroscientific approaches, interactive technologies, and cognitive measures have been applied in architectural design, and how these contributions can inform inclusive, evidence-based frameworks for human–space interaction. Automated searches were performed in IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus, complemented by manual searches in high-impact journals and conferences, yielding 1,426 records. After applying inclusion and exclusion criteria and validating reviewer consistency with Fleiss' Kappa, 32 primary studies published since 2017 were included. Data extraction followed structured criteria addressing types of spaces, employed technologies, measurement techniques, cognitive variables, disciplinary focus, user groups, and practical applications. Results reveal a predominant focus on urban and educational settings, whereas hospital, residential, and cultural spaces remain underexplored. Commonly used technologies include sensors, spatial computing, and virtual reality. Measurement techniques often combine simulations, questionnaires, and physiological recordings. Regarding cognitive measures, attention, comfort, and welfare were most frequently reported, while memory and spatial orientation were less frequently reported. Despite growing interdisciplinary interest, most studies remain conceptual or methodological, with few empirical validations in real-world contexts and a lack of studies using interactive technologies to integrate behavioral, physiological, and self-reported data. This review synthesizes current knowledge and offers guidance for future innovation toward inclusive, user-centered architectural environments that support cognitive and emotional welfare.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847389</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847389</link>
        <title><![CDATA[Beyond the black box: interpretability, accountability, and responsible clinical integration of AI-driven heart rate variability models—a narrative review]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Alexandru Burlacu</author><author>Maria Olariu</author><author>Oana Geman</author><author>Adrian Iftene</author><author>Roxana-Elena Bogdan-Goroftei</author><author>Crischentian Brinza</author>
        <description><![CDATA[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 limited interpretability, unclear accountability, and challenges in workflow integration, particularly for black-box models used in high-stakes settings.MethodsThis narrative, concept-driven review examined conceptual, methodological, clinical, and governance dimensions of interpretability in AI-driven HRV prediction. A structured literature search was performed in PubMed/MEDLINE, Scopus, and Embase databases.ResultsThe analysis showed that interpretability is not a binary property but varies by model design and deployment context. Post-hoc explainability methods may increase transparency, yet they can also be unstable, incomplete, or misleading, with potential to increase automation bias. Clinical adoption is further limited by signal-quality variability (ECG vs. wearable PPG), insufficient external validation, workflow misalignment, and unclear medico-legal responsibility. A four-step pragmatic implementation framework is proposed: data governance and signal integrity; robust model development and validation; workflow-compatible clinical integration with human oversight; and continuous post-deployment monitoring and governance.ConclusionHRV-AI systems should be treated as socio-technical interventions. Responsible adoption requires proportional transparency, explicit accountability structures, and lifecycle governance beyond predictive accuracy alone.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1818011</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1818011</link>
        <title><![CDATA[A DBSAHO scheme for smooth movement in heterogeneous network-based distributed mobility systems]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Prabha Mahenthiran</author><author>Dinakaran Muruganandam</author>
        <description><![CDATA[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 latency, packet loss, signaling overhead, and out-of-order packet delivery. The Dual Buffer Signaling Aggregation (DB-SAHO) was an earlier mechanism proposed to improve handover performance in NB-DMM homogeneous environments. The paper further extends the DBSA concept to heterogeneous networks and proposes a heterogeneous H-DBSA scheme that facilitates vertical handovers across access technologies. The proposed method uses a dual buffer service and aggregates signaling across diverse access areas. During transition, a forward buffer preserves incoming messages from the previous access network while the new buffer ensures a continuous, ordered transfer of data from the target network. At the same time, signaling aggregation reduces the number of control messages transmitted during handover. The proposed scheme is tested for performance using OMNeT++ simulations at different mobility speeds, and for sensitivity analysis with different buffer sizes and signaling delays. Simulation results show that the heterogeneous H-DBSA scheme significantly decreases latency during handover, minimizes packet losses and out-of-order rearrangements, and enhances throughput more than it increases signaling costs compared to the existing NB-DMM. The sensitivity results also reveal that performance is stable throughout various operating scenarios, showing the capability of the proposed mechanism in heterogeneous distributed mobility contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1803271</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1803271</link>
        <title><![CDATA[TRUSTLab dataset: a real-world CICFlowMeter dataset for IoT/edge intrusion detection]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Antonio Villafranca</author><author>Igor Tasic</author><author>Maria-Dolores Cano</author>
        <description><![CDATA[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 operational testbed reproducing enterprise-grade services and modern application interfaces. The dataset follows a single-class session policy, whereby each capture contains exclusively benign traffic or a single attack family, preventing temporal overlap and ensuring label integrity at the bi-flow level. The dataset includes 15 attack families spanning volumetric flooding, reconnaissance, application-layer exploits, protocol manipulation, evasive techniques, and persistence vectors. Traffic was processed into 16 single-class files totaling approximately 4.6 million bi-flows with 80 features per flow.ResultsComprehensive statistical analyses confirm the presence of discriminative signals without requiring payload inspection. A baseline binary classifier achieved an Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.9676 and a recall of 0.95, supporting the dataset’s utility for lightweight, edge-oriented IDS evaluation. The multiclass benchmark further reported per-family precision, recall, and F1-scores, with the main residual confusion concentrated in low-and-slow and HTTP-based vectors.DiscussionBy enforcing session-level class separation and preserving bi-flow label integrity, TRUST Lab provides a reproducible dataset for evaluating IDS models in IoT and edge environments. The dataset is publicly available to support further research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770179</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770179</link>
        <title><![CDATA[Beyond data sharing: enhancing IoT intrusion detection with blockchain-enabled federated learning]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aditya Durgadas Naik</author><author>Raj Mani Shukla</author>
        <description><![CDATA[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 lack of transparency regarding the shared local models and the aggregation techniques employed. This lack of transparency can potentially lead to model poisoning attacks and hinder collaborators from using alternative aggregation methods that better align with their specific use cases. To address this issue, this study proposes a blockchain-based approach using FL to ensure transparent, immutable records of model updates, thereby bolstering security and trust for intrusion detection in IoT devices. In contrast to traditional synchronization- or periodic-update-based approaches, this study proposes a novel time-independent aggregation method for FL blockchain, enabling greater flexibility. Furthermore, the proposed blockchain allows various users to utilize their own aggregation methods, rather than a fixed one, based on their needs, resources, and availability. We also developed a user interface for the proposed blockchain system to visualize various aspects of the method, such as model aggregation. The proposed system is tested using traditional metrics, such as AI model performance, as well as extensive user testing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1814498</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1814498</link>
        <title><![CDATA[Platform engineering and internal developer portals: a multivocal literature review]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Mateen Ali Anjum</author>
        <description><![CDATA[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 reputable venues that address platform engineering directly, while gray literature from foundations, vendors, and industry surveys is abundant. This study presents the first multivocal literature review (MLR) of platform engineering and internal developer portals, following published guidelines for including gray literature in software engineering reviews. The review synthesizes 88 sources across both peer-reviewed and gray literature, with sources explicitly tiered by provenance and gray literature assessed using the AACODS framework. Five research questions address the state of the literature, architectural components and patterns, success metrics and KPIs, adoption barriers, and the relationship between platform maturity and developer productivity. The synthesis yields a taxonomy of internal developer portal components grounded in 36 architecture-focused sources, an integrated metrics framework spanning DORA, SPACE, and developer experience dimensions, a comparative analysis of four platform engineering maturity models, and a quantification of the academic–practitioner divide: only 2 of 88 included sources (2.3%) originate from tier-1 venues with platform engineering as their primary topic, while practitioner communities have generated the authoritative definitions, frameworks, and measurement instruments as academic engagement lags by two to three years. A particularly striking gap concerns scorecards, the primary governance mechanism within IDPs, for which no peer-reviewed empirical evidence of effectiveness exists despite widespread commercial adoption. These findings carry implications for both researchers and practitioners. For the research community, the study identifies nine specific opportunities where empirical work is most needed, with a validated PE maturity model and a PE-specific measurement instrument representing the highest-impact contributions. For practitioners, the evidence supports treating platforms as products, combining delivery metrics with developer experience surveys, and designing golden paths as enablers instead of mandates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1737008</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1737008</link>
        <title><![CDATA[The role of explainability throughout the MLOps lifecycle: review and research agenda]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sule Tekkesinoglu</author><author>Matthias Wagner</author><author>Per Runeson</author>
        <description><![CDATA[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 subareas: data handling (data quality, data pre-processing, and data management), model development (training and pre-deployment auditing), and deployment (developer oversight and end-user interfacing). We identified several key touchpoints within each subarea where XAI methods address specific technical and operational challenges. The synthesis covers a wide range of topics, from explainable imputation and data filtering to fairness auditing in high-stakes decision-making. Findings reveal that although explainability is widely applied, it remains fragmented, with insufficiently validated reliability, and limited operationalization for regulatory compliance. Building on this analysis, we propose a research agenda for embedding continuous explainability throughout MLOps pipelines. Key directions include connecting explainability touchpoints across lifecycle phases, validating the reliability of XAI methods, and operationalizing explainability to meet regulatory requirements such as those defined in the EU AI Act. By framing explainability as an infrastructural mechanism for assurance rather than a post-hoc diagnostic feature, this work advances a lifecycle-spanning perspective on trustworthy and governable AI systems.]]></description>
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