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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>2026-10-24T03:10:18.348+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1994804</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1994804</link>
        <title><![CDATA[Attributable federated DDoS mitigation for 6G cloud network slices]]></title>
        <pubdate>2026-10-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anukriti Naithani</author><author>Seema Rawat</author><author>Pradeep Kumar</author>
        <description><![CDATA[6G enabled cloud networks segregate traffic into service slices (eMBB, URLLC, mMTC) with very different latency, reliability, and density needs. For the ever evolving network environments like such, DDoS defense must not only detect attacks but respond to them per slice, without breaking each slice's service guarantees. Detection is well studied, but mitigation remains unexplored. The existing federated slice-aware frameworks report only aggregate performance, without showing which stage of the pipeline produced the given outcome. This research proposed a federated, slice aware framework for DDoS defense in 6G Cloud networks. A deep detector segregates each flow and passes its confidence and embedding to per slice mitigation policies. It is Trained by federated aggregation across heterogeneous cloud edge domains so that no raw traffic leaves its source. The interface is confidence gated, which means low-confidence flows are allowed before the slice policy is consulted. This makes every decision attributable to either the gate or the policy. This research introduces a stage attributable evaluation that traces each mitigation outcome to its source, and a SLA metric that separates policy quality from the attack density of the test data. Three dataset configurations are usd in this research : A synthetic 6G CTGAN-generated dataset, CICDDoS2019 dataset and a combined configuration. Across three dataset configurations the framework detects DDoS traffic with 0.96 to 0.9998 accuracy. It mitigates attacks at up to exact malicious block and benign allow rates and raises sustained SLA from as low as 0.13 to 0.81 against oracle ceilings of 0.97. All results come from a single reproducible run, and an honest ablation shows a shared policy matches the per slice design on accuracy. The result is a privacy preserving, slice aware, and interpretable approach to DDoS mitigation for 6G cloud networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1854879</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1854879</link>
        <title><![CDATA[Grounded theory insights into human insider threats: a qualitative study of expert perceptions]]></title>
        <pubdate>2026-10-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Marcus L. Green</author><author>Priya D. Dozier</author><author>Eric Owusu</author>
        <description><![CDATA[Human insider threats continue to increase as digitalization grows, especially with the rise of remote work. Most studies of cybersecurity enhancements focus on technological solutions; however, the costliest cybersecurity events originate from human and insider threat risk factors. This novel, qualitative study identified drivers of human factor insider threats while addressing a need for in-depth, qualitative studies based on expert perceptions. This study conducted semi-structured interviews with 15 cybersecurity experts across government, healthcare, and cybersecurity sectors. A grounded theory methodology strengthened the analysis of codes and themes to generate dominant factors related to drivers and solutions of insider threats that led to developing a Human Insider Threat (HIT) framework. The HIT framework has practical implications for managers to assess insider threat vulnerabilities and for researchers to create scalable instruments to evaluate emerging insider threats. The HIT framework can support organizations in identifying human risk factors, improving security training programs, and aligning technology access controls with employee roles.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1915137</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1915137</link>
        <title><![CDATA[Structurally conditioned counterfactual generation and validation for question answering from scientific text]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ivan P. Malashin</author><author>Dmitry A. Martysyuk</author><author>Vadim S. Tynchenko</author><author>Andrei P. Gantimurov</author><author>Vladimir A. Nelyub</author><author>Aleksei S. Borodulin</author>
        <description><![CDATA[IntroductionA large language model (LLM)-based pipeline is presented for structured processing of scientific abstracts and for generating paired factual and counterfactual question-answer (QA) instances under document-level intervention constraints.MethodsThe implemented system extracts entities, events, and relations; induces a lightweight structural causal model (SCM) over scientific variables such as method, data, intervention, outcome, and metric; generates five factual QA pairs per abstract; rewrites them under one selected intervention; and filters inconsistent counterfactuals. The source export contains 100 Scopus abstracts, of which 67 documents with complete pipeline artifacts form the validation cohort. Generation uses gpt-4.1, while automated validation uses gpt-5-mini under a 15-criterion checklist; three human annotators independently score the same 67 documents.ResultsHuman agreement is moderate, with a mean pairwise Cohen's kappa of approximately 0.54. Automated validation is high for original QA and relation-level correctness (0.97 and 0.89) but very low for document-level global consistency (0.07), revealing a substantial gap between local correctness and cross-stage coherence. Under the score-based intervention analysis, 25.4% of documents exhibit a validation decrease above the predefined threshold.DiscussionWe refer to this quantity as the intervention degradation rate rather than as bias propagation, because the interventions concern scientific-content variables rather than protected attributes. The study evaluates generation quality, intervention sensitivity, and cross-stage consistency; it does not train a foundation model or claim training-time bias mitigation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847791</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847791</link>
        <title><![CDATA[Impact of information architecture on the accessibility of the UDG Biblioteca Digital: a user-centered usability evaluation]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michelle Anaya-Chora</author><author>Adrián Antonio Cisneros-Hernández</author><author>Marcela Del Rocío Ramírez-Mercado</author>
        <description><![CDATA[This article examines the impact of information architecture on the accessibility and usability of the Biblioteca Digital of the Universidad de Guadalajara (UDG), the university's principal digital gateway to scholarly information for its upper-secondary, undergraduate, master's, and doctoral communities. The platform provides access to more than 117 databases, nearly 90,000 electronic journals, more than 4.2 million electronic books, and over 44,000 theses, among other academic materials. Despite this extensive collection, many undergraduate students report persistent difficulty understanding the platform's structure and navigation; however, no prior study had examined empirically how the Biblioteca Digital's information architecture shapes this experience. To address this gap, a sequential mixed-methods design was implemented across five stages: descriptive log-file analytics, remote usability testing, a UX-writing and terminology-comprehension questionnaire, closed card sorting, and an evidence-based redesign of the navigation structure. The analytics stage established that 85.80% of platform accesses corresponded to undergraduate students, who were consequently treated as the platform's primary user profile. Usability testing (n = 24) showed that no participant completed every assigned navigation task successfully, evidencing structural friction rather than unfamiliarity with the platform. The terminology questionnaire (n = 58) showed that, while 65% of respondents could define the concept of an institutional repository, 93% could not correctly identify the contents of the online catalog and 84% were unfamiliar with Boolean operators. Closed card sorting (n = 20) revealed that participants consistently grouped the platform's 30 predefined content elements by function and everyday use rather than by the library-science taxonomy currently in place. These convergent findings were used to redesign the platform's navigation menus, simplifying labels and reorganizing resource groupings around users' mental models. The proposal was presented to, and favorably received by, the Biblioteca Digital's coordination team as a feasible, implementable improvement, although it has not yet undergone post-implementation validation. This study contributes empirical, institution-specific evidence on how information architecture affects accessibility in an academic digital library and offers a replicable, user-centered methodology that other institutional repositories can adopt to align institutional taxonomies with users' cognitive strategies for information retrieval.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1869476</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1869476</link>
        <title><![CDATA[The potential of bio-inspired trace alignment for side-channel analysis]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Nikolaos Alachiotis</author><author>Marco Ottavi</author><author>Bruno Endres Forlin</author><author>Konstantinos Paraskevopoulos</author><author>Luca Mariot</author><author>Ana-Lucia Varbanescu</author>
        <description><![CDATA[Public-key cryptography is vulnerable to mathematical attacks by quantum computers, and governments encourage organizations to adopt new post-quantum cryptography (PQC) algorithms to secure critical infrastructure. However, these new algorithms are susceptible to physical attacks, such as side-channel attacks, and current testing infrastructures to assess their resistance to physical attacks typically sacrifice quality for run time to cope with the high computational costs. In this work, we show that unprotected PQC implementations are vulnerable to Side-Channel Analysis (SCA), and discuss the hypothesis that bio-inspired methods and algorithms can enhance the effectiveness of SCA-based security evaluations. Building on preliminary empirical evidence from a previous SCA study of a public-key cryptographic implementation using the Advanced Encryption Standard (AES), we discuss their potential to improve security assessment against SCA attacks as a promising direction for future research. Early results show that a multiple sequence alignment (MSA) algorithm for DNA and protein sequences (MAFFT) can be used to align AES power traces, and the resulting alignment enabled the extraction of cryptographic keys with up to 44% fewer traces, when applying Correlation Power Analysis, than the alignment obtained through an elastic alignment implementation (based on Dynamic Time Warping) in a commercially available SCA tool. These early results provide preliminary support for the feasibility of the proposed hypothesis in AES, and motivate the exploration of MSA-based trace-alignment techniques in security evaluations of post-quantum implementations to assess whether the observations obtained based on AES transfer to PQC as well.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1896171</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1896171</link>
        <title><![CDATA[BGAFS-UNet: adaptive feature fusion via boundary guided sampling for retinal vessel segmentation]]></title>
        <pubdate>2026-10-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Guoce Zhang</author><author>Zhezhe Zhu</author><author>Aizimaiti Xiaokaiti</author>
        <description><![CDATA[IntroductionRetinal vessel segmentation is difficult because fundus images contain branching structures, scale variation, uneven illumination, lesions, and weak vessel boundaries.MethodsWe propose BGAFS-UNet, combining Boundary Perception Module (BPM), Lightweight Wide Gated Attention (LWGA) with Dynamic Channel Allocation (DCA) and Adaptive Branch Fusion (ABF), Cross-Stage Fusion (CSF), Contextual Mixing (ContMix), and Attention-Guided Dynamic Sampling (AGDS). We evaluate three prespecified image-disjoint runs on DRIVE, STARE, and CHASE and use image-level paired tests with Holm correction on DRIVE.ResultsThe full model obtains F1 scores of 80.61% on DRIVE, 76.15% on STARE, and 79.53% on CHASE. On DRIVE, corrected improvements are metric- and vessel-scale-specific: boundary F-score, clDice, and large-vessel Dice are significant, whereas Dice and small-vessel Dice are not.DiscussionBGAFS-UNet is parameter-efficient but not unconditionally more efficient, with higher FLOPs and latency than U-Net; the findings support structure-aware segmentation while motivating broader cross-device validation and optimization. The code and reproducibility materials remain available at https://gitee.com/Zhangguoce/bgafs-net.git, fixed at commit 477b217990f68d43d5c1c8f3a818d8cf2c8af384.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1972880</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1972880</link>
        <title><![CDATA[An integrated AI-based software architecture for load forecasting, controlled anomaly detection, and predictive energy management]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karlygash Alibekkyzy</author><author>Gulnur Massabayeva</author><author>Michał Majka</author><author>Madina Bazarova</author><author>Daniyar Kaliyev</author><author>Talshyn Keribayeva</author><author>Saule Belginova</author><author>Alina Bugubayeva</author>
        <description><![CDATA[The increasing deployment of smart meters and Internet of Things technologies has created a demand for intelligent computational systems capable of jointly forecasting electricity consumption, detecting abnormal behavior, and optimizing load management. This study proposes an integrated artificial intelligence-based framework for automated energy metering and consumption management in smart-grid environments. The framework combines a Long Short-Term Memory network for short-term load forecasting, an autoencoder-based model for anomaly detection, and Model Predictive Control for demand-side optimization. The system architecture includes data acquisition, communication, preprocessing, predictive analytics, anomaly monitoring, and decision-support layers. The primary empirical evaluation used 4,380 measured hourly observations (approximately 6 months) from a single administrative building. A separately flagged synthetic temporal extension was retained only as an auxiliary dataset and was excluded from all primary performance metrics. Across five independent model initializations, the final LSTM achieved a MAPE of 3.423 ± 0.555%, an RMSE of 0.336 ± 0.048 kW, and an R2 of 0.973 ± 0.007 on the 633-sequence independent measured test subset. The synthetic extension was maintained as a separately identified auxiliary component and was excluded from all primary training, validation, and independent test metrics. Using a validation-selected reconstruction-error threshold, the autoencoder detected 12 of 30 controlled anomalies in the independent test subset, yielding 100.00% precision, 40.00% recall, and an F1-score of 57.14%. Under the same controlled protocol, One-Class SVM achieved higher recall (53.33%) and F1-score (69.57%), indicating that the autoencoder should be interpreted as an architectural component rather than as the best-performing anomaly detector for the present dataset. A historical proof-of-concept MPC simulation, retained for architectural illustration, reported a 15% reduction in simulated peak demand and a 12% reduction in estimated electricity cost; these values are not interpreted as measured outcomes from the administrative-building experiment. Controlled anomaly injection was used to establish reproducible validation and test labels for the anomaly-detection component. The results demonstrate that the joint integration of forecasting, anomaly detection, and predictive control provides a computationally coherent framework for supporting energy-management efficiency, operational reliability, and data-driven decision-making in intelligent energy systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1946723</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1946723</link>
        <title><![CDATA[Constructing t-way test suites: a systematic survey and qualitative synthesis of the One-Parameter-at-a-Time approach]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Muhammad Sabo Yahaya</author><author>Ahmad Sobri Hashim</author><author>Hussaini Mamman</author><author>Shamsu Abdullahi</author><author>Umama Umer</author><author>Dahiru Adamu Aliyu</author><author>Idrissa Mohammed Godowoli</author>
        <description><![CDATA[Parameter interactions are a major source of software failures, yet the exponential growth of possible configurations makes exhaustive testing impractical. Combinatorial Interaction Testing (CIT) is a highly effective sampling technique for identifying software defects caused by parameter interactions. Over the past two decades, the One-Parameter-at-a-Time (OPAT) constructions paradigm has emerged as a cornerstone of CIT test suite generation. Although numerous OPAT strategies have been proposed to optimize test suite size and execution speed, the literature remains fragmented, and direct side-by-side quantitative benchmarking has been impeded by a lack of open-source tooling and standardized platforms. To address this, this study presents a systematic survey and qualitative synthesis of 40 primary OPAT studies published between 2000 and 2026, retrieved from four major digital libraries. Rather than attempting a direct quantitative benchmark comparison of execution times, which is shown to be mathematically invalid due to highly heterogeneous self-reported hardware environments and the closed-source nature of most academic tools, we establish a unified taxonomy that classifies OPAT research into five major categories: deterministic refinements, scaling and distribution strategies, advanced feature-enhanced strategies, metaheuristic and hybrid strategies, and specialized OPAT adaptations. Our analysis synthesizes qualitative trends, structural characteristics, parameter-order growth mechanisms, and feature-integration gaps across the literature. We conclude with a critical assessment of the tooling and reproducibility challenges in the field, outlining vital future pathways, including diversified metaheuristic and hyper-heuristic techniques, deterministic parallel algorithms, and AI-driven adaptive test generation, to transition OPAT research into industrial-grade, open-source software engineering pipelines.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1960635</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1960635</link>
        <title><![CDATA[TraceRank: configurable review support for requirements traceability recovery]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hamad Alsawalqah</author><author>Ahmad Abadleh</author><author>Yazn Alshamaileh</author><author>Marwan Al-Tawil</author><author>Ibrahim Aljarah</author><author>Mohammed Saad Alaydaa</author>
        <description><![CDATA[IntroductionRequirements traceability recovery is commonly evaluated as a single ranking task, although practical review settings differ in the evidence available before review, the inspection budget, and the possibility of analyst feedback. We present TraceRank, a configurable review-support framework that makes recovery assumptions explicit and enables only the recovery modes that are valid for the available evidence and review process.MethodsTraceRank separates cold-start recovery, partial-trace recovery, selective validation, fixed-budget review, and interactive feedback. It organizes retrieval, semantic-structural, dependency, hypergraph, validated-link, and feedback evidence within a common recovery pipeline. We evaluate TraceRank on five traceability datasets: eTour, iTrust, SMOS, eANCI, and LibEST.ResultsIn the cold-start setting, the best retained configuration recovers 404 of 501 true links at Top-30 (LinkRecall@30 = 80.6%) and completes 33 of 60 held-out requirements. Under the partial-trace assumption, the Validated Trace Responsibility Graph (VTRG) recovers 465 links (92.8%), completes 43 requirements, and reduces severe misses to 12. Across the same five requirement-level split seeds used for the split-robustness analysis, VTRG achieves mean LinkRecall@30 of 91.3% (SD 4.0%) and mean CompleteRate@30 of 76.5% (SD 8.1%). Archived fixed-budget and simulated-feedback outputs are retained only as exploratory review-process sensitivity evidence because the originating row-level priority queue and sequential update history are not preserved in the clean archive; they are therefore not treated as confirmatory evidence in the primary conclusions.DiscussionThe results support a configuration-aware interpretation of trace recovery: cold-start, partial-trace, fixed-budget, and feedback-based recovery are different operating settings and should not be interpreted as one universal static ranking problem.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937475</link>
        <title><![CDATA[Designing expandable digital learning ecosystems through student-generated immersive learning environments]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Oleksandra Bulgakova</author><author>Viacheslav Zosimov</author><author>Andrii Kudriavtsev</author>
        <description><![CDATA[IntroductionImmersive technologies and student participation have transformed digital education by enabling learners to contribute not only to learning activities but also to the development of digital learning environments. However, existing studies primarily focus on individual gamified applications or immersive learning experiences, while the long-term evolution, reuse, and institutional integration of student-generated environments remain insufficiently explored.MethodsThis study introduces a three-component framework comprising a Transformation Model, an Algorithmic Extension Layer, and an Expert-Supported Quality Assurance Layer. The framework formalizes the evolution of learner-generated educational artifacts into expandable digital learning ecosystems, formalizes the structural integration and reuse of educational fragments, and provides an expert assessment of educational quality and readiness for institutional integration.ResultsThe framework was illustrated and assessed using two educational cases: a structured immersive learning ecosystem based on a 3D simulation environment and an open collaborative learning ecosystem implemented through a Minecraft-based virtual campus. Both cases received very high overall expert ratings, while the criterion-level expert ratings indicated complementary assessed strengths in their capacity to support ecosystem development and institutional integration.DiscussionThe proposed framework illustrates a transition from individual immersive applications toward expandable digital learning ecosystems that support continuous learner contributions, institutional reuse, and long-term development. It provides a foundation for future research on scalable and sustainable ecosystem-oriented learning environments in higher education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1889642</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1889642</link>
        <title><![CDATA[A hybrid deep Kronecker and BN-eLU-AlexNet framework for early prediction of learner outcomes in e-learning platforms]]></title>
        <pubdate>2026-10-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chesta Malkani</author><author>Neetu Mittal</author><author>Subhranil Som</author>
        <description><![CDATA[IntroductionEarly prediction of learner outcomes in online learning environments can support timely academic intervention and learner support.MethodsThis study proposes DKBNE-AlexNet, a hybrid architecture combining a Deep Kronecker Network branch, a feature-token batch-normalized eLU AlexNet-inspired branch, gated residual fusion, and a wide linear residual path. Using the Open University Learning Analytics Dataset, 24,153 enrolments from 22 module presentations and 22,234 learners were analyzed. Eighty-four predictors were constructed from learner characteristics, course design, and engagement during the first 25% of each presentation. The target was the weighted mean of tutor-marked and computer-marked assessment scores. Models were evaluated using five-fold cross-validation grouped by module presentation and repeated three times.ResultsDKBNE-AlexNet achieved an MAE of 11.424 (95% group-bootstrap CI 10.389-12.560), RMSE of 14.843, and R2 of 0.117. Random Forest and LightGBM achieved slightly lower MAEs of 10.916 and 10.918, respectively, but the presentation-level difference between DKBNE-AlexNet and Random Forest was not statistically significant (Wilcoxon p = 0.388; Holm-adjusted p = 1.000). Distinct learning materials accessed and active learning days were among the strongest predictive contributors.DiscussionDKBNE-AlexNet provides a competitive and stable framework for early learner-outcome prediction, while strong tree-based models remain important baselines. The framework is best interpreted as an early-warning learning-analytics tool rather than a direct measure of teacher effectiveness.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1900515</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1900515</link>
        <title><![CDATA[Enhancing university network security through ML-based detection of DDoS and ARP spoofing attacks: a systematic review]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Kirungi Richard</author><author>Bashir Olaniyi Sadiq</author><author>Venkateswarlu Maniniti</author>
        <description><![CDATA[IntroductionUniversity networks are increasingly vulnerable to Distributed Denial-of-Service (DDoS) and Address Resolution Protocol (ARP) spoofing attacks due to open-access architectures, Bring Your Own Device (BYOD) practices, Internet of Things (IoT) integration, and dynamic traffic patterns. This study systematically reviews machine learning (ML)-based approaches for detecting these threats in university and related network environments.MethodsFollowing PRISMA 2020 guidelines, peer-reviewed studies published from January 2021 to January 2026 were identified through IEEE Xplore, SpringerLink, ScienceDirect, Taylor & Francis Online, and Web of Science, with the final database search completed on 31 January 2026. Of 400 identified records, 200 studies met the predefined eligibility and quality criteria and were included in the synthesis. The review examined supervised, unsupervised, semi-supervised, deep learning, hybrid, and ensemble approaches with respect to detection performance, validation strategies, deployment readiness, computational efficiency, adaptability, and explainability.ResultsThe findings indicate that many reviewed studies reported strong detection performance across supervised, unsupervised, deep learning, hybrid, and ensemble approaches. Hybrid and ensemble models frequently demonstrated strong reported results; however, these findings were not directly comparable across studies because of differences in datasets, attack scenarios, feature sets, validation procedures, and experimental environments. Benchmark datasets were used in 48% of the reviewed studies, whereas only 12% evaluated real university networks. Real-time detection was assessed in 40% of studies, while continuous learning, concept-drift detection, and model adaptation received comparatively limited attention.DiscussionThe evidence does not establish universal superiority of any particular learning paradigm and suggests that reported performance depends substantially on the evaluation context and methodological conditions. These findings highlight the need for adaptive, context-aware, cross-layer, and explainable intrusion detection approaches capable of addressing the operational characteristics of university networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1898906</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1898906</link>
        <title><![CDATA[From monolingual virtual exchange to AI-supported multilingualism: a critical perspective]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Judit Háhn</author>
        <description><![CDATA[Virtual Exchange (VE) has become a key approach in higher education for fostering intercultural competence through online collaboration. However, despite its inclusive framing, many VE initiatives continue to rely on English as a lingua franca or other shared-language arrangements, which may reproduce linguistic hierarchies and unequal participation. This paper examines the potential of AI-supported multilingualism to rethink predominantly monolingual approaches to VE. It argues that while generative AI and translation technologies may reduce communicative barriers and enable broader participation, they do not automatically transform underlying language ideologies or power relations. In doing so, it highlights a tension between accessibility and the interactional work through which intercultural learning occurs. Drawing on Critical Virtual Exchange, relational learning, translanguaging theory, and ethical interculturality, the paper proposes a critical perspective on AI-mediated VE. It further explores how AI may support linguistic equity, multilingual participation, and translanguaging-oriented meaning-making in Virtual Exchange. At the same time, it critically addresses risks such as technological solutionism, linguistic bias in AI systems, and the reinforcement of dominant language ideologies. It concludes that AI-supported VE should be grounded in critical pedagogy and linguistic justice. In this way, VE is reframed not as a site of frictionless communication, but as a relational and ethically accountable educational practice.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1895843</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1895843</link>
        <title><![CDATA[TCNNISTD: a parallel transformer–CNN network for infrared small target detection]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kaiheng Dai</author><author>Qiong Song</author><author>Qian Li</author><author>Chen Cheng</author><author>Yining Huang</author>
        <description><![CDATA[IntroductionInfrared small target detection is critical in applications such as missile guidance, security surveillance, and precision tracking. Conventional methods relying on manual feature engineering often fail under severe background clutter and noise, while deep learning-based methods tend to degrade small-target features in deep networks, especially in noisy conditions.MethodsWe propose a novel dual-branch architecture that combines Transformers and CNNs in parallel for robust infrared small target detection, focusing on low-signature scenarios. The CNN branch uses large-kernel convolutions to expand the receptive field and enhance local feature discrimination, while the Transformer branch captures long-range global dependencies. An Adaptive Fusion (AF) module with dual attention (channel and spatial) dynamically optimizes feature representation by amplifying target-specific features and suppressing background noise.ResultsExperiments show that our method achieves competitive performance against state-of-the-art models, with higher Dice coefficient and mean Intersection-over-Union (mIoU) scores on the augmented NUAA-SIRST dataset, indicating more precise segmentation boundaries and improved robustness in cluttered environments.DiscussionThese advantages highlight the potential of our approach for general infrared small target detection tasks. The dual-branch design and adaptive fusion strategy make it well suited to mission-critical applications such as security surveillance, and precision tracking, and it may also extend to remote sensing and maritime surveillance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1911927</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1911927</link>
        <title><![CDATA[Reusing obsolete windows 10 PCs for on-premises large language model inference]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ian Curington</author><author>Kevin Lano</author>
        <description><![CDATA[The Windows 10 end-of-life provides a material incentive to repurpose legacy PCs as on-premises large language model (LLM) inference servers. This study assesses the technical feasibility, economic viability and the environmental and social issues of converting an enterprise workstation with an NVIDIA Graphics Processing Unit (GPU) into a dedicated AI node. Using Proxmox virtualisation, Ollama, and Open WebUI to benchmark several open-source LLMs, this study shows that a repurposed workstation, fitted with a single consumer-grade NVIDIA GPU, can deliver stable inference throughput of 29–65 tokens/s for bounded, conversational coding and document-assistant workflows, while preserving full data sovereignty and eliminating recurring subscription fees. Performance analysis reveals that resource contention and GPU memory allocation are the primary determinants of throughput. Correctness, assessed against three established public benchmarks with gold answers, does not track the same ranking as throughput; combining both axes identifies gemma4:e4b as the strongest model evaluated on this hardware for both speed and correctness, while the slowest model tested was among the most accurate. A cost-benefit model shows local operation is substantially cheaper than mid-range and premium cloud alternatives, and only modestly costlier than the cheapest cloud tier on a per-prompt basis—a gap this study argues is outweighed by the digital-sovereignty benefits of local control, particularly where compliance-driven data-handling constraints apply. The study summarizes key background motivations for on-premises AI—privacy, security, regulatory risk—and estimates that this repurposed workstation's usage-phase CO2e footprint is lower than each of the cloud alternatives compared, in addition to avoiding the embodied manufacturing emissions of new hardware. Repurposing legacy workstations with readily available open source tools offers a sustainable, cost-effective pathway for organizations to deploy practical LLM inference capabilities locally, providing a strategic alternative to cloud services for entities that prioritize privacy, regulatory compliance or long-term cost containment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919950</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919950</link>
        <title><![CDATA[From model trust to software topology: a Perspective on structurally governed agentic AI systems]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Michai Morin</author><author>Fahim Sufi</author>
        <description><![CDATA[Large language models are increasingly embedded in agentic software systems that retrieve information, plan subtasks, call tools, modify code, execute tests, and adapt through memory or feedback. This transition changes the safety problem: when a model acts through software tools, failure can alter external state. This Perspective argues that agentic AI safety should move beyond exclusive reliance on model trust, alignment, or prompt-level defenses and should also be treated as a software-architecture problem. We propose software topology as a design lens for structurally governed autonomy and develop a five-plane reference topology comprising intent, orchestration, execution, oversight, and adaptation. Its core invariant is that components that decide should not directly act, and components that act should not act without supervision. The topology is realized in Polos, an open reference specification that encodes role contracts, message schemas, declared flow graphs, bounded loops, governed self-improvement, and validator-checked invariants. We argue that this approach complements model alignment and empirical red teaming by providing an inspectable substrate for evaluating, constraining, and governing tool-using AI agents.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1901490</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1901490</link>
        <title><![CDATA[DepressNet: an interpretable machine learning framework for depression severity stratification using psychometric data]]></title>
        <pubdate>2026-10-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anika Rahman</author><author>Fahmid Al Farid</author><author>Md Humaion Kabir Mehedi</author><author>Jia Uddin</author><author>Hezerul Bin Abdul Karim</author>
        <description><![CDATA[Depression is a highly prevalent mental health condition worldwide, affecting approximately 332 million individuals and contributing significantly to global disability burden. In computational mental health research, machine learning approaches are increasingly applied to structured psychometric questionnaires; however, many existing studies formulate the problem as binary classification, which fails to capture the ordinal nature of symptom severity represented in standardized instruments. This study presents a computational psychometrics framework for depression severity stratification using the Depression Anxiety Stress Scales (DASS-42) questionnaire dataset. The proposed model, DepressNet, four base learners—XGBoost, Random Forest, LightGBM and a calibrated support vector machine (SVM)—with a PyTorch-based multi-layer perceptron meta-learner trained on out-of-fold probability features to reduce information leakage and improve generalization. To address class imbalance inherent in real-world psychometric data, the training pipeline incorporates SMOTETomek resampling together with class-weighted cross-entropy loss. The framework is evaluated on 39,775 real-world questionnaire responses under an imbalanced five-class classification setting using multiple complementary metrics. On a held-out test set, the model achieves 85.38% accuracy, 59.01% macro F1-score, 0.7158 Matthews Correlation Coefficient, and 0.9714 AUC-ROC. It is acknowledged that the severity labels are derived from the same DASS-42 items used as input features; consequently, the framework learns structured scoring patterns within psychometric data rather than predicting independent clinical ground truth. The Mild severity class remains the most challenging stratum (F1 = 0.10), reflecting inherent symptom overlap with adjacent classes and the persistent difficulty of classifying a narrow four-point severity band. Beyond predictive evaluation, a SHAP-based explainability module is applied to examine both global and class-wise feature attribution patterns across severity levels. The analysis provides interpretable relationships between questionnaire items and predicted severity categories, highlighting structured symptom-response patterns captured by the model. Overall, this work contributes a reproducible and interpretable machine learning framework for psychometric data analysis, with emphasis on severity stratification, ensemble-based learning, and explainable modeling of mental health questionnaire responses.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1939596</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1939596</link>
        <title><![CDATA[AI-assisted animation versus text-based description: divergent pathways to intercultural understanding of Norwegian, Mising, and Javanese greetings on a 3R-virtual intercultural stage]]></title>
        <pubdate>2026-10-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Normoda Doley</author><author>Fan Yang</author><author>Berta Esti Ari Prasetya</author><author>Kaori Takamine</author><author>David Dalsky</author>
        <description><![CDATA[Human greetings at initial encounters are often viewed as simple formulaic expressions, yet in many languages they embody culturally unique worldviews that are often lost in English translation. This study investigates three such greetings— Norwegian Det er hyggelig å møte deg, Mising kumríksudung, and Javanese sugeng—within a structured AI-supported virtual intercultural exchange on a 3R-Virtual Intercultural Stage based on “3Rs of Social Psychology” (i.e., Role, Rank, and Room) and an Equivalent Indigenous Concept Model. Thirty-one university students from Norway, India (Mising), and Indonesia (Javanese) were organized into six cohorts, each randomly assigned to one of two groups during a two-week exchange: an AI-animation Group (n = 16), which used AI-assisted scripted Vyond animations to visually depict their indigenous greetings, and a Writing Group (n = 15), which created text descriptions of the greetings in Google Docs. Analysis of post-task reflections suggests that participants in the AI-assisted Group reported stronger evidence of intercultural understanding through encoded, non-verbal cues (e.g., gestures and character posture) that emerged when critiquing inaccuracies in their AI-generated animations—a disruption that slowed and deepened processing, which the authors term a “Pedagogical Speed Bump.” Writing Group participants, by contrast, developed understanding primarily through sustained textual explanation, with comparatively less embodied elaboration. Although both groups appeared to reach a similar general level of intercultural understanding, they arrived there through markedly different processes, suggesting that the collaborative medium in virtual exchange (AI-assisted vs. text-based) functions not merely as a delivery method but as a cognitive mediator in its own right, shaping how intercultural understanding is constructed rather than how it is conveyed. This distinction carries important implications for intercultural education and the preservation of indigenous languages.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1928487</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1928487</link>
        <title><![CDATA[A same-condition decision-risk audit for CPU-only machine-learning efficiency measurement]]></title>
        <pubdate>2026-10-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Khudran M. Alzhrani</author>
        <description><![CDATA[CPU-only machine-learning efficiency is commonly reported as wall-clock time, throughput, estimated GFLOP/s, or effective bandwidth. This paper introduces a same-condition decision-risk audit that evaluates whether such reported differences are supported by repeated-measurement evidence. The audit constructs two estimates from repeated measurements taken under the same documented condition and quantifies how often their relative difference exceeds a practical relative-difference threshold. We apply the audit to eight CPU-only workload scenarios—dense numerical computation, memory movement, framework-level inference, and single-step training—executed across two local Windows environments and one managed Google Colab CPU allocation. Despite consistent functional-output summaries, timing and derived-metric dispersion depend strongly on workload role, execution context, and phase policy. Under a controlled measurement condition with a fixed phase policy, cross-block same-condition comparisons exceeded a 10% relative-difference threshold in 77.6%–80.6% of cases when reporting a single timing observation, and in 67.1%–76.1% when using the mean of 10 observations, across five representative single-thread scenarios. Approximate sample-size estimates indicate that 81–110 timing observations per group would be needed to support a 10% target relative difference in wall-clock time under the observed variability. The audit demonstrates that CPU-only efficiency differences can only be interpreted meaningfully when the measurement context, phase policy, block structure, same-condition A/A threshold-exceedance, and estimated observation counts are explicitly reported.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1817408</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1817408</link>
        <title><![CDATA[Neuromorphic-classical hybrid framework for real-time augmented reality processing: adaptive stream fusion with dynamic threshold modulation and uncertainty-aware sensor integration]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Akey Sungheetha</author><author>Oluwasegun Julius Aroba</author><author>Rajesh Sharma Rajendran</author>
        <description><![CDATA[Contemporary augmented reality systems must reconcile three competing demands: latency low enough to remain imperceptible; visual fidelity high enough to convince a viewer that virtual content belongs in the physical scene; and power draw low enough to enable extended mobile use. This study narrows its focus to three specific bottlenecks that recur across conventional pipelines and proposes an algorithmic remedy for each, evaluated through implementation and controlled synthetic execution rather than field deployment. The first bottleneck is feature extraction, which conventional pipelines were found to spend 42% of total processing time on. The second is unreliable multimodal sensor fusion, whose spatial-tracking accuracy, as the same profiling found, could degrade by 68% when lighting or motion departs from laboratory conditions. The third is reactive content delivery, whose 180 ms loading delays break immersion each time a user turns their head. The design targets that motivated these numbers—3D molecular visualization at 120 ± 3.2 Hz for education, anatomical overlays precise to 1.1 ± 0.15 mm for remote surgical guidance, assembly-instruction positioning within 1.8 ± 0.2 mm for industrial maintenance, and 28 ± 4 ms contextual content delivery for location-based entertainment—are presented strictly as potential applications suggested by this algorithmic evaluation, not as demonstrated outcomes. Validating the framework on commercial hardware such as Xreal One Pro, Apple Vision Pro, and Meta Quest Pro remains a planned next step beyond the present algorithm-level, synthetic-dataset scope.]]></description>
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