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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>2026-07-11T11:03:05.612+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1853976</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1853976</link>
        <title><![CDATA[Wearable sensors reveal the reality gap: a full-body motion dataset for validating VR cycling simulators]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jonas Pöhler</author><author>Antonia Vitt</author><author>Florian Wolling</author><author>Florian Michahelles</author><author>Kristof Van Laerhoven</author>
        <description><![CDATA[Virtual reality cycling simulators are increasingly used in research on urban mobility, rehabilitation, and road safety, yet their biomechanical fidelity remains under-validated due to the lack of standardized, publicly available datasets. We present a novel benchmark resource comprising synchronized full-body inertial measurement unit (IMU) data at 120 Hz and egocentric video from 10 participants who cycled an identical 1.4 km urban route in both Vienna and a motion-enabled VR simulator. Six body-worn sensors captured head, torso, arm, and leg movements, enabling detailed comparisons of pedaling rhythm, limb coordination, balance control, and visual attention patterns across environments. Our analyses reveal that VR successfully replicates fundamental locomotor patterns including pedaling cadence and bilateral leg coordination. However, significant differences emerge in upper-body dynamics: participants exhibited greater torso rotational variability in VR compared to real-world cycling, suggesting altered balance strategies. Head movement patterns showed broader yaw angles during virtual riding, yet these differences did not correlate with simulator sickness or immersion ratings. Emotional assessments indicated lower enjoyment and higher frustration in VR, highlighting gaps in affective realism despite mechanical similarity. This openly available dataset provides a critical resource for validating simulator designs, developing personalized VR training systems, and advancing research in embodied locomotion. The dataset, analysis tools, and documentation are freely accessible at: https://street2simulator.de/.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855449</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855449</link>
        <title><![CDATA[Digital twin-based virtual reconstruction and immersive experience design of traditional Miao ethnic costume patterns]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qingchuan Huang</author><author>Mohan Singh Ajmera</author>
        <description><![CDATA[This study aims to propose and evaluate the digital twin framework for preserving the traditional patterns of ethnic minority groups, especially the traditional costumes of the Miao ethnic minority. The framework emphasizes the integration of cultural knowledge using flexible digitalization technology in the preservation of textiles. The digital twin framework proposed includes the following: the enhanced neural radiance field with physically based material modeling and cultural semantic embedding, the immersive multi-modal platform, and the culturally sensitive knowledge graph system. The data set used was composed of 140 carefully chosen textile items, which were taken from the Europeana, Sketchfab, and DeepFashion3D data sets based on their technical similarity to specific Miao cultural items. The results showed quantifiable improvements compared to state-of-the-art baselines in geometric reconstruction accuracy and rendering quality. Additionally, the study on the use of the framework by 115 participants indicated a considerable improvement in the retention of cultural knowledge using virtual reality, augmented reality, and web-based technologies. Furthermore, the results of semantic retrieval verified the potential of the framework to examine culturally important heritage beyond the scope of visual similarity matching. This provides methodological recommendations for the digital preservation of intangible textile heritage and lays a foundation for possible direct collaboration with the Miao source communities. Specifically, the framework addresses three gaps identified in flexible textile heritage preservation: (i) the assumption of rigid geometry and Lambertian appearance in current 3D pipelines, which is mitigated by a physically based material branch coupled to a neural radiance field; (ii) the separate evolution of immersive delivery and content semantics, which is mitigated by a culturally grounded knowledge graph that propagates meaning into both rendering and retrieval; and (iii) the absence of open-access 3D corpora for under-resourced traditions such as the Miao, which is mitigated by a transparent proxy-validation protocol that quantifies technical equivalence prior to community-engaged acquisition.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1884141</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1884141</link>
        <title><![CDATA[SAE-MRHL: a Self-Adaptive Explainable Meta-Reinforcement Hybrid Learner for remaining battery life prediction in IoE systems]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>D. Gayathri</author><author>S. P. Shantharajah</author>
        <description><![CDATA[The rapid evolution of the Internet of Everything (IoE) has accelerated the deployment of battery-powered sensors for intelligent environmental monitoring and decision-making. In dynamic marine environments, accurate prediction of Remaining Battery Life (RBL) is critical to ensure system reliability while minimizing maintenance costs. However, conventional estimation models often fail to capture nonlinear discharge behavior and temporal drift induced by environmental factors such as temperature, turbidity, and salinity. To address these challenges, this study proposes SAE-MRHL, a Self-Adaptive Explainable-Meta Reinforcement Hybrid Learner that integrates ensemble regression, Long Short-Term Memory (LSTM) networks, and a TD3-based Adaptive Learning controller based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The framework dynamically adapts ensemble weights and learning rates in response to environmental drift while incorporating explainability through entropy-based feature importance, SHapley Additive exPlanations (SHAP) analysis, and uncertainty quantification. Experimental evaluation demonstrates that SAE-MRHL achieves superior predictive performance, with a coefficient of determination (R2) of 0.9383, Root Mean Squared Error (RMSE) of 0.1744, and Mean Absolute Percentage Error (MAPE) of 0.57%. Comparative analysis against baseline models, including LSTM, eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), confirms significant improvements in accuracy, adaptability, and robustness under non-stationary conditions. Overall, SAE-MRHL provides a unified, drift-aware, and interpretable framework for intelligent energy management in IoE systems, with strong potential for deployment in marine monitoring, smart cities, and autonomous industrial environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1860123</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1860123</link>
        <title><![CDATA[Quantum-ready IoMT architecture for chest X-ray imaging with lightweight CNN and hybrid chaos-DNA encryption]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Altahir Saad Ahmed</author><author>Ali Raza</author><author>Abed Saif Alghwali</author><author>Suzan Hassan Bakhit</author><author>Muhammad Farman</author>
        <description><![CDATA[This study presents a complete quantum-aware Internet of Medical Things (IoMT) framework for secure medical imaging that integrates lightweight edge diagnostics, quantum key distribution, and hybrid image encryption. At the edge layer, MicroRadNet, an ultra-compact convolutional neural network (CNN) with exactly 3,140 trainable parameters, is deployed on a Raspberry Pi Zero 2 W, achieving 98.24% accuracy on chest X-ray classification with INT8 quantization. This reduces the model size to 3.14 KB and yields inference latency that meets real-time constraints. At the communication layer, a gateway-mediated BB84 protocol executed on Amazon Braket generates session keys via Golay [24, 12, 8] reconciliation (correcting up to t = 3 errors per codeword) and privacy amplification, producing 256-bit keys with quantum bit error rate (QBER) < 0.02. At the image-protection layer, a hybrid cipher combines 5D hyperchaotic permutation (seeded from BB84 key material), fixed-rule DNA encoding, and quantum-keyed diffusion. Security evaluation demonstrates near-ideal entropy, strong NPCR and UACI values, uniform ciphertext histogram distribution, low directional adjacent-pixel correlations, low average absolute correlation, large key space, high plaintext sensitivity, high key sensitivity, and stable chaotic-sequence randomness. These results provide empirical statistical validation of resistance to common statistical, differential, brute-force, and key-sensitivity attacks. End-to-end latency remains below the 1.5 s system constraint, validating practical edge deployment. The framework replaces classical public-key dependency with simulation-validated quantum-aware foundations while maintaining clinical accuracy and real-time performance, establishing a path toward deployable, standards-aligned quantum-aware healthcare security.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841980</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841980</link>
        <title><![CDATA[A position estimation method of a wearable device using pulse wave peak time differences with a smartwatch]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ryuta Kotera</author><author>Kazuya Murao</author>
        <description><![CDATA[Wearable devices come in various forms. In addition to those designed for specific body parts, such as wristwatch, eyeglass, and ring devices, there are also wearable devices that can be attached to arbitrary body positions, such as instant tattoo-type and bandage-type devices. When a wearable device is attached to an arbitrary position on the body, it must recognize its attachment position to adjust its functionality and processing accordingly. In this paper, we propose a method for estimating the attachment position of a wearable device with an unknown position using pulse wave sensors mounted on both a device with a known position and one with an unknown position. Because the arrival time of pulse waves varies with distance from the heart, the time difference between pulse wave peaks at the wrist and another body part can be used to infer the attachment position. The proposed method estimates the device's attachment position by comparing pulse wave peak detection times between the wrist and the unknown body part. For evaluation, data were collected from 12 body positions: forehead, mouth, left ear, right ear, left upper arm, right upper arm, left wrist, right wrist, left finger, right finger, left toe, and right toe. The proposed method was evaluated under two conditions: using data collected on the same day and on different days. The results showed that when the number of target body parts was two, the maximum F1 score was 1 for both data acquired on the same day and on different days. In contrast, when all 12 body parts were used as targets, the maximum F1 score was 0.58 for data acquired on the same day and 0.29 for data acquired on different days.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837091</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837091</link>
        <title><![CDATA[LightFormer-3D: a lightweight hierarchical CNN-transformer hybrid for 3D medical image segmentation]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qingzheng Hu</author><author>Ji Li</author><author>Wenqiu Zhu</author>
        <description><![CDATA[IntroductionBrain tumor segmentation from 3D MRI is critical for treatment planning, yet deploying accurate Transformer-based models in clinical settings remains challenging due to prohibitive computational costs. State-of-the-art methods often exceed 90M parameters and require seconds per inference, precluding real-time use on resource-constrained hardware.MethodsWe propose LightFormer-3D, a lightweight hierarchical CNN-Transformer hybrid that applies a co-design principle to jointly optimize patch embedding, self-attention, and multi-scale feature fusion, ensuring that efficiency gains compound across all stages. Specifically, a Depthwise Separable Convolution Patch Embedding (DSC-PE) reduces embedding parameters by 70%; a Multi-head Self-Attention with Spatial Sequence Reduction (MSA-SR) compresses self-attention complexity from O(N2) to O(N2/r3); and a Lightweight Scalable Feature Fusion (LSFF) module adaptively weights multi-scale features using only five parameters.ResultsEvaluated on the BraTS 2017 and BraTS 2021 benchmarks, LightFormer-3D achieves a mean Dice score of 87.0% on BraTS 2021 with 1.78M parameters and 9.3 GFLOPs, surpassing UNETR (80.2% Dice, 92.5M parameters) by 6.8 pp and the best competing lightweight method, U-Net_ASPP_EVO (86.0%), by 1.0 pp, while delivering 10× faster inference than UNETR (0.51s vs. 5.41s per case on an NVIDIA RTX 4060). Five-fold cross-validation (86.7% ± 0.2%) confirms stable generalization. Ablation studies validate each module's contribution, and zero-shot external validation on the independent MU-Glioma-Post dataset (203 post-treatment cases) confirms robust whole-tumor localization (WT Dice 85.0%) under severe domain shift.DiscussionLightFormer-3D establishes that co-designed lightweight modules can simultaneously improve accuracy and efficiency, achieving a new Pareto-optimal point for brain tumor segmentation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1809919</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1809919</link>
        <title><![CDATA[DMFNet: exploring diverse mid-feature for visible-infrared person re-identification]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ganqing Mo</author><author>Yanbing Chen</author><author>Hairong Ye</author>
        <description><![CDATA[IntroductionVisible-infrared person re-identification remains a challenging task due to inherent modality discrepancies between RGB and infrared images. Existing methods often struggle to effectively capture both modality-specific and modality-invariant features simultaneously, limiting their cross-modal matching performance.MethodsThis paper presents DMFNet (Diverse Mid-feature Network), a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap. DMFNet integrates two key modules: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that produces diverse intermediate representations through Instance-Batch Normalization variants.ResultsExtensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that DMFNet achieves state-of-the-art performance, with significant improvements in Rank-1 accuracy (up to 8.2% on SYSU-MM01 and 6.5% on RegDB) and mean Average Precision (mAP) over existing methods.DiscussionOur approach not only enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process. These results pave the way for more robust person re-identification systems in real-world surveillance scenarios, particularly in low-light conditions where traditional visible-only systems often fail.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886274</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886274</link>
        <title><![CDATA[An explainable machine learning framework for academic stress classification among university students: a comparative multi-dataset study with ablation analysis and statistical significance testing]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Assel Omarbekova</author><author>Aizhan Nazyrova</author><author>Gulmira Bekmanova</author><author>Zhanar Lamasheva</author><author>Albina Gibadullina</author><author>Assem Abdikarim</author>
        <description><![CDATA[Academic stress among university students represents a major public health concern due to its detrimental effects on cognitive functioning, psychological well-being, and long-term academic performance. Although machine learning has increasingly been applied to stress prediction, existing studies commonly rely on single datasets, insufficiently address class imbalance, and provide limited model interpretability. This study proposes a comparative machine learning framework that integrates explainable artificial intelligence (XAI), ablation analysis, and statistical significance testing to improve the classification of student stress. Two complementary datasets were analysed. Dataset 1 included 777 university students aged 18–22 years with three stress-type categories (Eustress, Distress, and No Stress), while Dataset 2 comprised 1,100 records with 20 psychological, physiological, environmental, and academic features labelled according to three stress severity levels (Low, Medium, and High). Five supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine with a radial basis function kernel, and Multilayer Perceptron—were evaluated using 10-times repeated stratified 5-fold cross-validation. Severe class imbalance in Dataset 1 (91.4% Eustress) was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and its impact was quantified through a structured ablation study. Model performance differences were assessed using the Friedman test with post-hoc Nemenyi analysis, and SHAP was employed to provide global and class-specific model interpretability. The Friedman test demonstrated statistically significant differences among classifiers (χ² = 68.97, *p* < 0.001), with post-hoc analysis identifying Random Forest and Logistic Regression as the statistically superior-performing models. Random Forest achieved the highest overall classification accuracy (0.8909) and the lowest log loss (0.2057), whereas Logistic Regression produced the highest discriminative performance (AUC-ROC = 0.9853). The ablation study confirmed that SMOTE substantially improved minority-class prediction performance. SHAP analysis identified blood pressure, sleep quality, teacher–student relationship, and academic performance as the most influential predictors of overall stress severity, while self-esteem, sleep quality, bullying, and anxiety level were the strongest contributors to the High Stress class. The proposed framework provides a reproducible, interpretable, and statistically validated approach for multi-class stress classification among university students. By combining class imbalance correction, rigorous comparative evaluation, explainable artificial intelligence, and statistical validation, the framework addresses important methodological limitations of previous studies and offers a robust foundation for the development of early stress detection and intervention systems in higher education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1876401</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1876401</link>
        <title><![CDATA[Use of technological resources in the era of artificial intelligence and its effect on academic performance]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuri Reina Marín</author><author>Omer Cruz Caro</author><author>Judith Nathaly Alva Tuesta</author><author>Lenin Quiñones Huatangari</author><author>Roger Angeles Sánchez</author><author>Jorge Luis Maicelo Guevara</author><author>River Chávez Santos</author>
        <description><![CDATA[The advancement of digital technologies and generative artificial intelligence (GenAI) has transformed learning environments in higher education; while tools such as ChatGPT offer personalized experiences and immediate feedback, their relationship with academic performance remains insufficiently understood in university contexts. This study analyzed the use of technological resources in the AI era, considering frequency of use, perceived usefulness, familiarity, and type of use, as well as their contribution to university students’ academic performance and identifying technological and contextual factors associated with it. A data-mining approach was employed, applying 10 supervised regression algorithms using 10-fold cross-validation, complemented by interpretability analysis using SHAP values to identify relevant predictors. Regularized regression models (Elastic Net; Lasso) showed the best relative predictive performance. The SHAP analysis revealed that the field of study (≈ 0.41) and study cycle (≈ 0.34) are the dominant predictors, while the use of AI for academic writing (≈ 0.10) emerged as the technological variable with the greatest positive contribution to academic GPA within the model. Academic performance was mainly associated with structural factors related to students’ educational trajectories, while AI tools showed a complementary role. The predictive contribution of technology appears to depend more on pedagogical integration than on frequency of use alone. These findings suggest the need to integrate digital strategies into higher education, implementing them in a differentiated manner by discipline and academic level, and promoting the guided and purposeful use of AI and technological resources in learning processes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873568</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873568</link>
        <title><![CDATA[Adversarial attacks detection for network intrusion detection systems using outlier-filtered principal component analysis]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>N. Dhinakaran</author><author>S. Anto</author>
        <description><![CDATA[Cybersecurity frameworks are increasingly incorporating machine learning-based Intrusion Detection Systems (IDS) into their security measures. Despite the effectiveness of these systems, they remain susceptible to different forms of attacks that take advantage of their operation; specifically, those that are designed to circumvent their protective mechanisms. For example, modifications made to network traffic can produce “adversarial samples,” which are designed to go undetected. To tackle this issue, two systems based on Principal Component Analysis (PCA) have been proposed for spotting adversarial samples: Standard Principal Component Analysis (SPCA) and Outlier Filtered Principal Component Analysis (OFPCA). SPCA identifies the basic structure of normal network traffic through principal components and detects adversarial attacks by looking at reconstruction errors. A sample is projected onto the principal components and then reconstructed in the original space. The difference between the original and reconstructed features is the reconstruction error. Larger errors can indicate manipulation. OFPCA, on the other hand, is trained only on normal samples after removing outlier data points from the training set. When testing SPCA method using the NSL-KDD dataset, it achieved an AUC-ROC score of 0.97 in detecting FGSM adversarial samples. OFPCA had a higher AUC-ROC score of 0.99 in identifying FGSM adversarial samples. OFPCA performed better than SPCA and other techniques, when tested under different adversarial attacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1707808</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1707808</link>
        <title><![CDATA[MoDiPO: text-to-motion alignment via AI-feedback-driven direct preference optimization]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Massimiliano Pappa</author><author>Luca Collorone</author><author>Giovanni Ficarra</author><author>Indro Spinelli</author><author>Fabio Galasso</author>
        <description><![CDATA[Diffusion Models have revolutionized the field of human motion generation by offering exceptional generation quality and fine-grained controllability through natural language conditioning. Their inherent stochasticity, that is the ability to generate various outputs from the same input prompt, is key to their success. However, this diversity should not be unrestricted, as it may lead to unlikely generations. Instead, it should be confined within the boundaries of text-aligned and realistic generations. To address this issue, we propose MoDiPO (Motion Diffusion DPO), the first methodology to adapt Diffusion Direct Preference Optimization to align text-to-motion diffusion models. We streamline the laborious and expensive process of gathering human preferences needed in DPO by leveraging AI feedback instead. This enables us to experiment with novel DPO strategies, using both online and offline generated motion-preference pairs. To foster future research we contribute with a motion-preference dataset which we dub Pick-a-Move. We demonstrate, both qualitatively and quantitatively, that our proposed method yields significantly more realistic motions. In particular, MoDiPO achieves statistically significant improvements in Fréchet Inception Distance (FID) of up to 39% on MLD/HumanML3D and consistent gains of 9%–15% across both MLD and MDM on HumanML3D and KIT-ML. Finally, MoDiPO secures a threefold increase in preference from human evaluators compared to the original models' outputs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1816972</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1816972</link>
        <title><![CDATA[An application of selective coding to create a theoretical framework on the factors that influence success or failure in adopting the SIG-UFRN software ecosystem]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ailson Medeiros Vasconcelos</author><author>Sandro Ronaldo Bezerra Oliveira</author>
        <description><![CDATA[IntroductionWe call Software Ecosystems (SECOs) the development of software around a common technology platform that results in a set of solutions or services for a consumer market. The Integrated Management System (SIG-UFRN) of the Federal University of Rio Grande do Norte (UFRN) is a technological platform that forms part of the UFRN SECO and is used by a considerable number of Federal Educational Institutions (FEIs) throughout Brazil. The scenarios of use of the technology platform involve a complex mix of technical, social, and business aspects that can lead to the adoption or rejection of the SIG-UFRN. If these aspects are not observed, they can harm the business activities of the FEI, waste resources, and result in penalties for public managers who make poor decisions. This research aimed to construct and validate a theoretical framework that represents the adoption of the SIG-UFRN based on technical, business, and social factors.MethodsTo achieve this objective, Selective Coding, as defined in the Grounded Theory (GT) method, was used, as well as Focus Groups for validation. Selective coding is the third stage of the GT. This refines and integrates concepts to construct a theoretical framework for a phenomenon. This coding process enabled the refinement and interrelation of the concepts inherent in the adoption scenarios of the SIG-UFRN. These concepts were identified in the previous open and axial coding stages of GT. Using techniques such as Storyline Memo, Core Category, and Paradigm Model, a theoretical framework could be constructed representing the adoption of SIG-UFRN.ResultsWe identified a theoretical framework that explained the adoption process of the SIG-UFRN, which was then validated with stakeholders.DiscussionThe results of this study could influence the success or failure of the adoption process and guide stakeholders in adopting SIG-UFRN best practices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1775185</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1775185</link>
        <title><![CDATA[A cartography of human experience across physical and digitally mediated environments: a scoping review]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Burcu Nimet Dumlu</author><author>Yüksel Demir</author>
        <description><![CDATA[IntroductionArchitecture has long drawn on science, philosophy, and the arts to understand and shape human experience in relation to the built environment. As architectural spaces increasingly incorporate interactive, adaptive, and digitally mediated systems, experience extends beyond physical settings toward hybrid configurations in which bodies, technologies, and environments jointly shape perception and action. Across architecture, Human–Computer Interaction (HCI), and Human–Building Interaction (HBI), however, the conceptual vocabulary of experience remains fragmented.MethodsThis scoping review maps how human experience has been theorized and operationalized in relation to the physical environment and examines how experiential concepts are extended, adapted, and transformed under digital mediation, with a particular focus on extended reality (XR), including virtual, augmented, and mixed reality. Using a hybrid search strategy anchored in foundational scholarship on human experience and expanded through citation chaining, berrypicking, and systematic database searches, we identified experiential terms used to study human–environment and human–building relations. A secondary analytic stage examined how these terms shift under digitally mediated conditions, including changes in embodiment, multisensory perception, agency, and social presence.ResultsThe review identified 107 experiential terms associated with the physical environment and 44 terms whose definitions explicitly depend on digitally mediated spatial contexts. The resulting conceptual cartography reveals continuities, transformations, and emergent experiential forms across physical and digital environments. The analysis highlights how digital mediation reshapes established experiential concepts while also generating new forms of experience that cannot be fully explained through existing physical-environment frameworks alone.DiscussionBased on these findings, we propose the concept of an experience realm, framing experience as a dynamic field co-constituted by humans, environments, and technologies. This integrative perspective provides a conceptual foundation for future research in architecture, XR, and HBI, supporting more coherent interpretation, comparison, and design of embodied and multisensory environments across physical and digitally mediated contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1898963</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1898963</link>
        <title><![CDATA[Editorial: Artificial intelligence for technology enhanced learning]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Maha Khemaja</author><author>Dalel Kanzari</author><author>Antonio Sarasa Cabezuelo</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873627</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1873627</link>
        <title><![CDATA[Formal schedulability analysis for LLM inference: TTFT and TBT deadline guarantees via response-time theory]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amine Marref</author><author>Khaled Tarmissi</author><author>Hassene Chaibi</author>
        <description><![CDATA[IntroductionLarge language model (LLM) inference systems operate under strict latency service-level objectives (SLOs): a deadline on the first output token (TTFT) and a maximum inter-token interval (TBT). Real-time (RT) scheduling theory provides formal schedulability tests, worst-case response-time (WCRT) bounds, and admission control, yet existing LLM schedulers ignore these tools entirely.MethodsWe present Chronos, the first schedulability framework for LLM inference grounded in RT theory. A two-phase task model maps each request to a sporadic prefill job with a TTFT deadline and a decode task whose iterations are bounded by the TBT SLO. From this model we derive a closed-form WCRT bound for TTFT, a TBT capacity condition, a prefill–decode interference analysis, and a sound admission control algorithm proved correct analytically. We validate Chronos with a discrete-event simulator parameterised by roofline-model estimates for a 7B-parameter model on an A100 GPU. Each experiment replays 50,000 requests sub-sampled from a one-week, 44-million-request Azure LLM Inference production trace.ResultsAt 10× nominal load, where admission-uncontrolled schedulers reach 99.4% TTFT miss rates, Chronos maintains zero misses while admitting 53.9% of arrivals. At 5× load it admits 88.9% of requests with P99 TTFT of 161 ms—more than 2× below uncontrolled baselines. No admitted request ever misses its TTFT or TBT deadline.DiscussionThese results demonstrate that classical RT scheduling constructs translate directly and effectively to LLM inference, providing formal, verifiable latency guarantees that heuristic schedulers cannot offer.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892105</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892105</link>
        <title><![CDATA[Editorial: Artificial Intelligence and emerging technologies for inclusive and innovative education]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Vladimir Robles-Bykbaev</author><author>Sergio Luján-Mora</author><author>Martín López-Nores</author><author>Salvador Otón Tortosa</author><author>Mary Sánchez-Gordón</author><author>Ricardo Mendoza-González</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855908</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855908</link>
        <title><![CDATA[Exploring inclusive AI in education: perceptions of neurodivergent and neurotypical students]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Berber Pieterson</author><author>Bo Sichterman</author><author>Supraja Sankaran</author><author>Robert-Jan Korteland</author><author>Bart Wagemakers</author><author>Stan Van Ginkel</author>
        <description><![CDATA[The adoption of AI in higher education is considered promising for enhancing the inclusivity of learning trajectories for neuro-diverse student populations. However, concerns are raised about potential biases in the design features of AI-based applications, which may neglect or misrepresent the specific characteristics and learning needs of neuro-divergent students. This study presents the perceptions of neuro-divergent and neuro-typical students (N = 18) regarding the inclusivity of the AI-based application Honest Mirror. This prototype was designed to enhance students’ presentation skills by providing AI-based feedback. A qualitative approach was adopted, and data were collected using semi-structured interviews. Data were analyzed according to the three design principles of the framework for inclusive AI learning design: engagement, representation, and action and expression. First, concerning engagement, results suggest that mainly neuro-divergent students emphasize the lack of human warmth in AI-based feedback. Second, regarding representation, neuro-divergent students seem to prefer visually minimal, calming color schemes that help sustain their attention. Further, both groups of students indicate that the explainability of the AI system and automated feedback remains unclear. Third, regarding action and expression, both groups of students emphasize that the application is easy to use intuitively. However, suggestions for improvements concern adding explanations of the different steps and actions throughout the application. From a scientific perspective, the findings of this empirical study indicate that inclusivity of AI-based applications might be promoted by incorporating a sense of human warmth into the feedback process. Additionally, understanding the perceptions of neuro-divergent and neuro-typical students regarding different aspects of inclusivity might help to refine theoretical design features of AI-based applications. Practically, this research provides valuable preliminary insights for creating AI-based applications that aim to promote inclusivity among diverse student populations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1870451</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1870451</link>
        <title><![CDATA[Human-AI collaborative lesson design is associated with enhanced student outcomes and planning quality in secondary physical education: a randomized experimental study]]></title>
        <pubdate>2026-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amayra Tannoubi</author><author>Tore Bonsaksen</author><author>Fairouz Azaiez</author>
        <description><![CDATA[BackgroundArtificial intelligence (AI) planning tools are increasingly introduced in physical education (PE); however, empirical evidence regarding their pedagogical value remains limited.ObjectiveThis study aimed to compare the effects of three PE lesson design conditions—AI-only, Human-only, and Hybrid (AI + teacher)—on students’ basketball shooting performance, documented lesson-plan quality, student enjoyment, and planning efficiency.MethodsNinety secondary school students (Mage = 14.7 ± 1.2 years; 50% girls) were randomly assigned to three groups (n = 30 each). All participants completed a six-session basketball shooting unit delivered by the same certified PE teacher. The primary outcome was shooting accuracy (/30). Secondary outcomes included documented planning quality assessed via the Assessing Quality Teaching Rubrics (AQTR) and student enjoyment measured using the Physical Activity Enjoyment Scale (PACES). Data were analyzed using one-way ANCOVA, Kruskal–Wallis tests, and repeated-measures ANOVA.ResultsAll groups demonstrated significant improvements from pre- to post-test. The Hybrid condition yielded the largest improvement in shooting performance (d = 1.42), followed by Human-only (d = 1.01) and AI-only (d = 0.78). A significant group effect was observed, with the Hybrid condition outperforming both alternatives. In terms of lesson design, the Hybrid condition achieved the highest total AQTR score, with the greatest advantage observed in the Instructional Guidance sub-dimension. Students in the Hybrid group also exhibited a significantly more positive trajectory of enjoyment across the six sessions compared to the other groups. Additionally, the Hybrid condition required substantially less planning time than the Human-only condition (−67%).ConclusionThe Hybrid approach was associated with superior student outcomes, higher-quality lesson planning, and greater planning efficiency. These findings support the use of generative AI as a pedagogical support tool within a co-design framework, rather than as a replacement for teacher expertise.Clinical trial registrationDoi: 10.17605/OSF.IO/Y4RZQ.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1865398</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1865398</link>
        <title><![CDATA[Network-aware communication-efficient fingerprint representation for resource-constrained IoT systems]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibrahim Alameri</author><author>H. I. Wahhab</author><author>Tawfik Al-Hadhrami</author><author>Sultan Noman Qasem</author>
        <description><![CDATA[Biometric authentication with Internet of Things (IoT) systems is constrained by low data bandwidth, packet size, and energy capabilities. The transmission of raw biometric data exceeds the maximum transmission unit (MTU) size of traditional IoT standards (e.g., IEEE 802.15.4), resulting in large packet fragmentation and a high frame collision probability. We present a network-aware payload optimization method to minimize the application layer payload. thereby reducing airtime (channel occupation time) and improving spectrum efficiency. By using skeleton bitmaps and minutiae vectors, the size of the data is decreased by 70%–98% compared with raw images. A smaller payload reduces the protocol header overhead and ARQ for lost packets in lossy wireless environments. We introduce a distributed edge computing architecture for offloading data-intensive tasks from the core network to the network edge, thereby reducing backhaul traffic. Performance tests with a network simulator (NS-3) in Wi-Fi 6 (IEEE 802.11ax) and IEEE 802.15.4 scenarios reveal that transmission times are significantly reduced from 420–520 to 150–190 ms and energy consumption from 110–160 to 65–95 mJ by reducing the payload size from 120 to 35 kB or even further down to 2.4 kB. These results indicate that network-aware, communication-efficient biometric data representations enable scalable and energy-efficient IoT authentication. This strategy emphasizes the importance of minimizing transmitted data volume through network performance metrics in limited wireless scenarios. The results provide architectural guidelines for designing secure and low-latency biometric-based authentication systems in smart homes, healthcare monitoring, and industrial IoT applications, emphasizing network-centric optimization for resource-constrained IoT networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830781</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830781</link>
        <title><![CDATA[Shot boundary detection for locating subliminal stimuli in films]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Javier Sanz-Aznar</author><author>Juan José Caballero-Molina</author><author>Carlos Aguilar-Paredes</author>
        <description><![CDATA[IntroductionWith the objective of detecting film shots with a duration that would qualify them as subliminal, this paper proposes a shot boundary detection system to locate shots in a video that last for only one or two frames.MethodsThis design prioritizes the complete retrieval of extremely short shots, minimizing false negatives, even at the expense of additional false positives. This is achieved by a sequential filtering strategy, integrating complementary metrics in a cascade, ensuring robustness against motion, luminance variations and noise while reducing the processing time by progressively ruling out potential cases for comparison. The series of processes adopted were a color histogram analysis with a dynamic threshold, a mean squared error analysis (MSE), a structural similarity index measure (SSIM) and a histogram correlation analysis. The system design was tested on 10 films in which the shots that had to be detected were known beforehand, and subsequently on 15 other films that may potentially contain subliminal shots.ResultsThe results reveal the effective detection of 100% of the shots, although the erroneous detections produced require a visual review at the end of the process. Finally, the analysis was applied to 1,247 films to verify, in a real-world scenario, whether the designed system is effective in detecting one- or two-frame shots.DiscussionIt was thus determined that the design proposed can facilitate the identification of short shots for a large number of films, meeting the initial objective of the design and confirming the usefulness of this system for detecting subliminal stimuli in films.]]></description>
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