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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>2026-06-19T09:44:16.852+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837023</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837023</link>
        <title><![CDATA[The human firewall effect training and awareness as drivers of phishing mitigation and reporting behavior]]></title>
        <pubdate>2026-06-19T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Omar Osman Haji Abdi</author><author>Ali Abdi Jama</author><author>Abdirahman Ibrahim Abdi</author>
        <description><![CDATA[Phishing attacks remain a major cybersecurity threat, particularly in environments where human factors play a critical role in system vulnerability. While organizations widely implement information security training and awareness programs, evidence on their effectiveness in promoting protective behavior remains inconsistent. This study examines the relationships among information security training, security awareness, phishing threat mitigation behavior, and reporting behavior within humanitarian organizations operating in Mogadishu, Somalia. A quantitative cross-sectional design was employed, and this study is presented as a Brief Research Report to provide concise and focused empirical evidence on these relationships. Data were collected from 121 employees using a structured questionnaire, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to analyze the proposed relationships. The results show that information security training significantly enhances security awareness and reporting behavior but does not directly influence phishing threat mitigation behavior. Security awareness emerged as the strongest predictor of both mitigation and reporting behaviors. Furthermore, mediation analysis revealed that security awareness fully mediates the relationship between training and mitigation behavior and partially mediates the relationship between training and reporting behavior. These findings highlight the importance of human-centered cybersecurity strategies, emphasizing continuous awareness-building rather than reliance on traditional training alone. The study contributes to theory by clarifying the role of awareness as a behavioral mechanism and provides practical implications for strengthening organizational resilience in fragile and resource-constrained contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1794972</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1794972</link>
        <title><![CDATA[Analyzing VR-based group discussions for timely speaking-intention feedback to leaders]]></title>
        <pubdate>2026-06-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chenghao Gu</author><author>Jiadong Chen</author><author>Tianyuan Yang</author><author>Feike Xu</author><author>Boxuan Ma</author><author>Shin'ichi Konomi</author>
        <description><![CDATA[IntroductionDespite advances in virtual reality (VR) devices, leaders in VR-based group discussions lack inclusive facilitation support. Speaking-intention cues are latent information indicating readiness to speak, yet they are difficult to perceive in VR. Although existing works show the possibility of detecting speaking-intention cues using sensors and machine learning, a key question remains: how should such cues be delivered to align with leaders' perceived needs?MethodsWe conducted a study of VR-based group discussion (N = 24) combining physiological sensing, behavioral coding of interaction dynamics, and post-hoc leader annotations and questionnaires.ResultsResults show that leaders most often desired feedback during relaxed baseline states with short-term physiological fluctuations, indicating active cognitive regulation rather than high stress or complete stability. Leaders preferred feedback not only during observational phases but also during leader-dominant facilitation. Questionnaire results further reveal a strong preference for duration-based intention cues and generally non-anonymous feedback, as well as concerns about information overload and social pressure.DiscussionWe finally discuss design implications for speaking-intention feedback in future VR systems supporting leadership and inclusive collaboration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1862641</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1862641</link>
        <title><![CDATA[MAC protocol for multi-hop wireless sensor networks utilizing integrated reinforcement learning with joint frame and slot optimization]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tayaba Keerio</author><author>Muhammad Sulleman Memon</author><author>Muhammad Shafique</author><author>Muhammad Bux Alvi</author><author>Aadil Chan</author><author>Akhtar Hussain</author><author>Muhammad Hafidz Fazli Bin Md Fauadi</author>
        <description><![CDATA[This paper introduces a distributed reinforcement learning-based MAC protocol designed for high-density educational IoT environments. In smart campuses, the reliability of real-time data from student wearable sensors and classroom environmental monitors is often hampered by hidden-node interference as well as network collisions. This phenomenon disrupts the synchronicity required for effective Human-AI collaboration. Current research utilizes an adaptive slot-swapping strategy to ensure stable, low-latency communication, facilitating seamless human-AI collaboration in data-driven pedagogical settings. The suggested protocol changes both temporal and spatial scheduling at the same time to keep up with changing traffic loads and changing multi-hop network topologies, compared to traditional methods, which often optimize these dimensions separately. Simulation results show that the proposed protocols maintain stable performance across diverse network densities and traffic loads of 0.1 to 1.0 Erlangs. Moreover, collision rates were reduced to 60% compared to Q-learning ALOHA in dense interference scenarios, thereby effectively mitigating the pseudo steady state trap. A 30–50% faster convergence time was demonstrated, ensuring that AI-driven educational feedback loops are established almost instantaneously. Significant enhancement in energy efficiency to 35%, reducing end-to-end delay. These results confirm that joint optimization of frame and slot parameters is a robust strategy for building the reliable, high-throughput communication backbones necessary for the next generation of AI-driven pedagogical tools.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1817034</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1817034</link>
        <title><![CDATA[Identifying knowledge gaps on the edge for visual question answering]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sarikaa Sridhar</author><author>Sriram Sai Ganesh</author><author>Goonmeet Bajaj</author><author>Christopher Myers</author><author>Srinivasan Parthasarathy</author>
        <description><![CDATA[Human Cognition is complex and highly sophisticated system capable of accomplishing astonishing feats, partly due to our ability to seek out and overcome “unknowns,” or gaps in our knowledge, skills, and capabilities. Artificial Intelligence (AI) systems often draw inspiration from human intelligence, however, they often lack the ability to recognize when their knowledge is insufficient, leading them to provide answers even when incorrect. This limitation poses significant challenges, particularly in human-AI teaming and edge AI scenarios, where systems may lack requisite knowledge of the environment. To address this, we propose Tiny Knowledge Gap Identification (TinyKGI), a lightweight framework for automatically identifying plausible cognitive skills that the model lacks (i.e., Knowledge Gaps; KGs), which could lead to incorrect predictions. Our framework leverages human cognitive skills to structure how AI models reason and to define the types of Knowledge Gaps they may plausibly exhibit. By identifying insufficient cognitive capabilities, TinyKGI enables the development of more reliable and robust AI systems. TinyKGI uses a deep learning approach to classify different types of KGs for multimodal reasoning tasks while enabling efficient inference in resource-constrained environments. Through model quantization, we significantly reduce the memory footprint and execution time, resulting in a compact and lightweight model. Evaluated on three datasets, TinyKGI improves Macro-F1 scores by up to 10% over the previous state-of-the-art, while achieving up to a 1.8 × speedup and a 4× reduction in memory usage. We further evaluate TinyKGI on an edge device (Jetson Nano), where it achieves a 1.7× speedup and a 3.9× reduction in memory with only a 0.1% degradation in Macro-F1 score.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1854937</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1854937</link>
        <title><![CDATA[Integrating citizen monitoring and intelligent technologies into a unified digital system for environmental water monitoring]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alex Karpov</author><author>Olga Mudrakova</author><author>Yulia Altunina</author><author>Dmitrii Goncharov</author><author>Vladimir Barinov</author>
        <description><![CDATA[BackgroundIn Russia, inland surface waters are controlled and monitored at the state level. The active development of industrial, economic, and household activities necessitates continuous and comprehensive oversight of water bodies. Effective implementation of these principles requires public involvement and the creation of a unified, clear, and accessible tool for recording and identifying problems, thereby ensuring a timely response to changes in aquatic ecosystem conditions. To process and manage large arrays of heterogeneous data, a digital system has been developed. This system integrates public monitoring, machine learning, the Internet of Things, and unmanned aerial vehicles to ensure transparent water body management processes, open decision-making, and accessible data for forecasting short-term and long-term changes in water resources and adjacent territories.Materials and methodsThis study focuses on water bodies, with the research subject encompassing their control and monitoring processes. To identify key characteristics, a systems analysis approach was employed, incorporating classification, decomposition, observation, business modeling, statistical analysis, and synthesis. Data visualization was achieved through graphical methods, providing a foundation for software engineering techniques and Event-Driven Architecture (EDA).ResultsThis article presents process models that support the digital transformation of water body monitoring. Based on these results, the system’s software and hardware architecture was defined, its minimum technical specifications established, and primary user roles identified. The paper also describes the functional capabilities of each service and the characteristics of the intelligent data processing models used.ConclusionThe findings align with existing research on digital process transformation. They hold both theoretical and practical significance for standardizing data, developing unified water management protocols, and ensuring seamless, centralized access to environmental information.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848519</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848519</link>
        <title><![CDATA[An OWL-based ontology for semantic competency mapping and explainable assessment in digital STEM teacher professional development systems]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karlygash Alibekkyzy</author><author>Madina Bazarova</author><author>Akmaral Sadakbayeva</author><author>Gulnaz Zhomartkyzy</author>
        <description><![CDATA[The digital transformation of education has increased the need for interoperable and machine-interpretable frameworks that can support competency-based teacher professional development in STEM-oriented environments. This study proposes an ontology-driven semantic framework for transparent competency mapping and explainable assessment in digital STEM teacher professional development systems. The model was implemented using the Web Ontology Language in the Protégé environment and validated through competency questions expressed as SPARQL queries. The proposed ontology integrates instructional methods, professional competencies, indicators, assessment tools, and control forms within a unified semantic architecture and enables automated reconstruction of the traceability chain linking pedagogical methods to measurable professional outcomes. In contrast to fragmented competency representations, the proposed framework introduces an integrated semantic traceability mechanism supporting explainable competency assessment and ontology-driven validation of professional development structures. The results show that the populated case-study ontology supports automated competency mapping, semantic correspondence matrix generation, detection of missing links, and consistency validation of the modeled relations. The study contributes an ontology-centered semantic framework for digital teacher professional development systems and demonstrates its practical relevance for learning analytics, competency monitoring, and adaptive teacher training environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855851</link>
        <title><![CDATA[Hybrid multimodal learning framework for crop disease detection, adaptive treatment, and price forecasting]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nilesh P. Sable</author><author>Vinod Kumar Shukla</author><author>Parikshit N. Mahalle</author><author>Vijayshri Khedkar</author>
        <description><![CDATA[Crop diseases play a significant role in food production globally; therefore, there is an urgent need to develop quick and accurate diagnostic techniques that are more effective than manual inspection methods. The proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection. This study also proposes a framework for pesticide recommendation and the treatment of plants. This study experiment on tomato and cotton crop leaf data for disease detection. Experimental results on a tomato crop disease detection dataset show that the proposed model shows high performance. EfficientNetB0 provides more stability and generalization capabilities in different scenarios compared to other models, such as YOLOv8, ResNet50, and a custom CNN model. The use of a knowledge-based decision support system provides sustainable pesticide recommendations based on environmental and symptom-specific parameters. Forecasting of pesticide prices through LSTM methods yields forecasts within 3.2% and 4.1% MAE, enabling improved decision-making by providing instant points of reference for potential price movements. Research uses SHAP and LIME to provide explainability to users, thus improving user buy-in through transparency. Overall, this modular system provides a data-driven decision-making model to improve the efficiency of managing crops.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1858284</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1858284</link>
        <title><![CDATA[Examining adoption intention toward blockchain-enabled AI-based gamification in education: a trust–motivation–value perspective]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wafa Naif Alwakid</author><author>Nisar Ahmed Dahri</author><author>Mamoona Humayun</author><author>Ghadah Naif Alwakid</author>
        <description><![CDATA[IntroductionThe integration of artificial intelligence (AI), gamification, and blockchain technologies is transforming digital learning environments by enabling personalized, engaging, and secure educational experiences. However, the adoption of AI-driven gamified systems remains constrained by concerns related to trust, transparency, and perceived value, particularly in contexts where human–AI interaction and system credibility are critical. Despite growing interest in these technologies, empirical research has yet to examine how trust-building mechanisms and motivational factors jointly influence user adoption in AI-driven educational systems.MethodsThis study proposes and empirically evaluates an Integrated Trust–Motivation–Value (TMV) framework to explain adoption intention toward blockchain-supported AI-based gamification in higher education. A quantitative cross-sectional survey was conducted with 300 participants who had prior experience with AI-based and gamified learning platforms. The model incorporates blockchain-related trust dimensions (security, transparency, data integrity, and decentralization) and gamification-driven motivational factors (points and badges, leaderboards, rewards and feedback, collaboration, and enjoyment). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess measurement validity and test hypothesized relationships.ResultsThe findings indicate that blockchain-related features significantly enhance trust, with decentralization, data integrity, and security emerging as key predictors, while transparency shows no significant effect. Gamification elements positively influence motivation, with perceived enjoyment identified as the strongest driver, followed by points and badges, and collaboration. In contrast, leaderboards, rewards, and feedback demonstrate non-significant effects. Both trust and motivation significantly contribute to perceived value, which in turn strongly predicts adoption intention. The model demonstrates substantial explanatory power, highlighting the central role of value formation in AI-driven educational technology adoption.DiscussionThe results highlighted the importance of balancing trust-building mechanisms and intrinsic motivational design in AI-driven educational systems. In the context of human–AI collaboration, learners’ perceptions of system reliability and meaningful engagement play a critical role in shaping adoption behavior. The findings suggest that not all technological and gamification features contribute equally, emphasizing the need for context-sensitive design strategies. Future research should explore hybrid approaches that integrate explainable AI, adaptive personalization, and trustworthy system design to enhance human–AI collaboration in education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1724158</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1724158</link>
        <title><![CDATA[From embodied dissent to commodified affect: an audio meme as feminist affective repertoire on TikTok]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Annabella Backes</author>
        <description><![CDATA[This article examines how feminist meanings and affective orientations are shaped, patterned, and circulated through TikTok’s memetic and affective infrastructures by focusing on the viral uptake of Paris Paloma’s song “Labour.” Rather than treating TikTok as a neutral distribution channel for feminist content, the article conceptualizes the platform as an infrastructure of feeling in which visibility, participation, and affective intensity are jointly organized through platform affordances and user practices. Building on this perspective, the study argues that audio memes function as affective repertoires: reusable sonic and memetic templates through which feminist meanings become recognizable, repeatable, collectivized, and economically legible within TikTok’s visibility economy. Empirically, the article traces the memetic trajectory of “Labour” across a qualitative dataset of 500 high-engagement TikToks sampled from ten widely circulated audio versions of the song. Drawing on the concept of affective registers, understood as recurring multimodal constellations of bodies, practices, and discourses, the analysis asks (RQ1) which affective registers crystallize around the song, (RQ2) how TikTok’s infrastructure of feeling shapes these registers, and (RQ3) what this trajectory reveals about the affective politics and economy of feminist meaning-making on the platform. The analysis identifies four recurring affective registers: refracted vulnerability, embodied dissent, accumulated grievances, and monetized affect. Across these registers, “Labour” operates as a shared sonic infrastructure that coordinates feminist storytelling, embodied performance, and affective alignment across dispersed creators and contexts. The findings show that memefication enables feminist meanings to circulate and resonate at scale by rendering them affectively recognizable and practically replicable, while simultaneously shaping the meanings and affective orientations that become publicly visible and economically valuable within TikTok’s platform environment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1826488</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1826488</link>
        <title><![CDATA[Human-centered adaptive e-learning: leveraging learner profiling and adaptive algorithms to enhance satisfaction in digital learning environments]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibrahim Adam</author><author>Tengku Putri Norishah Tengku Shariman</author><author>Zainudin Siran</author>
        <description><![CDATA[IntroductionThe growing demand for personalized digital learning has increased interest in adaptive e-learning environments that tailor instructional experiences to individual learners. Despite these developments, many online learning implementations still rely on standardized instructional designs that limit meaningful personalization and learner engagement. This study investigates the effectiveness of an adaptive e-learning intervention developed using a human-centered design approach, where learner satisfaction was used as a key indicator of personalized learning experiences.MethodsGuided by a design-based research approach, the study integrates the Felder–Silverman Learning Style Model and the Personalized Learning Design Framework to propose an adaptive e-content development mechanism that combines instructional design principles with learning style categorization. The proposed approach conceptualizes content as modular learning objects in which learning outcomes, instructional materials, and knowledge assessments are adaptively organized to support personalized learning pathways aligned with learners’ style preferences while allowing dynamic adjustments based on evolving learning needs. Two topics within an undergraduate course module were redesigned using this approach to incorporate adaptive instructional content and personalized learning pathways. A mixed-methods intervention study was subsequently conducted with 50 undergraduate students. Learner satisfaction was measured across four human-centered constructs: learner interface, content, learning community, and personalization. Quantitative data were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis, while a focus group discussion provided qualitative insights into learners’ experiences.ResultsThe regression model significantly predicted personalization, explaining 44.5% of the variance in learners’ perceptions. Content (β = 0.418, p = 0.016) and learning community (β = 0.467, p = 0.005) emerged as significant predictors, whereas learner interface was not significant. Focus group findings further highlighted the importance of adaptive learning materials and collaborative interaction in shaping personalized learning experiences.DiscussionThese findings underscore the importance of human-centered instructional design in developing effective adaptive e-learning environments. The study demonstrates that prioritizing adaptive content organization and building collaborative community pathways significantly enhances learner satisfaction and engagement in higher education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1843009</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1843009</link>
        <title><![CDATA[The power of agency: how interactivity in data storytelling influences emotional engagement and perceived trustworthiness in news]]></title>
        <pubdate>2026-06-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tien Hanh Hoang</author><author>Gijs Ipers</author><author>Sandy Claes</author>
        <description><![CDATA[As digital news continues to evolve, data visualization has become an important instrument for enhancing storytelling and fostering emotional engagement. Interactivity is often assumed to deepen this engagement, yet its effects on emotional response and perceived trustworthiness remain underexplored. This study examines the impact of interactivity in data storytelling using The Uber Game published by the Financial Times. A between-subjects experiment was conducted with 60 participants, randomly assigned to either an interactive game condition or a non-interactive video condition. Data were collected through Likert-scale questionnaires and open-ended responses. Results show that the interactive version significantly increases emotional engagement. It also affects perceived trustworthiness, although more moderately, with some participants expressing skepticism toward specific elements of the experience. These findings suggest that while interactivity can enhance engagement, its influence on trustworthiness is more complex and context-dependent, with implications for the design of interactive news experiences.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1829089</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1829089</link>
        <title><![CDATA[Grading at scale: privacy-preserving automated short answer grading with small language models on CPUs]]></title>
        <pubdate>2026-06-12T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Luca Saur</author><author>Adrienn Toth</author>
        <description><![CDATA[Automated Short Answer Grading (ASAG) is invaluable for scaling grading in an overloaded education system. However, current solutions offer a trade-off: proprietary Large Language Models (LLMs) like GPT-4o raise privacy concerns, while self-hosting requires expensive, hard-to-obtain GPU resources. To resolve this dilemma, this paper explores the use of Low-Rank Adaptation (LoRA) fine-tuned Small Language Models (SLMs, ≈1B parameters) against a zero-shot proprietary baseline (GPT-4o). GRAS, a new synthetic ASAG dataset, is introduced, and three experiments are conducted to evaluate performance, generalisation, and CPU inference feasibility of SLMs. The results show that fine-tuned SLMs achieve superior in-domain performance to GPT-4o (Macro-F1 up to 0.96 vs. 0.90 overall, and 0.96 vs. 0.75 in the AI domain). In cross-domain transfer, GPT-4o remained stronger (D1 → D2: 0.67κ2 vs. 0.43, D2 → D1: 0.93κ2 vs. 0.77), highlighting the generalisation limitation of SLMs. Furthermore, the feasibility of running SLMs on consumer CPUs was demonstrated, with Qwen3 0.6B grading 521 answers in 273.65s (0.52s per sample), potentially directly on the end user's device. These findings establish fine-tuned SLMs as a viable, low-cost, and privacy-friendly alternative to proprietary models for educational applications.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847059</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847059</link>
        <title><![CDATA[The devil is in the details: factors modulating the effectiveness of narrative privacy education video interventions]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Renita Washburn</author><author>Tangila Islam Tanni</author><author>Mary Jean Amon</author>
        <description><![CDATA[Social media interdependent privacy (IDP) violations involve users sharing information and media about others without consent. While psychosocial interventions have been proposed to increase awareness and reduce violations, prior research shows mixed results regarding the effectiveness of different intervention designs. We recruited 625 participants to test the effects of the following design elements in narrative-based privacy education videos: narrative perspective (third-person vs. first-person) and risk of consequences (low vs. high). We also tested intervention effectiveness based on individual (social media disorder; SMD) and image content (more-or-less private) differences. The interventions reduced the sharing of especially sensitive images compared to the control, but they impacted people and the types of content shared differently. In terms of individual differences, people with disordered social media use were more influenced by first-person narratives than third-person narratives, especially first-person narratives of everyday ‘low-risk’ violations. In contrast, people low in social media disorder were more impacted after relatively ‘high-risk’ third-person narratives, which was the least effective intervention for people high in social media disorder. The findings demonstrate the importance of presenting diverse examples in the privacy education sphere in order to target diverse user types, where failure to take into account psychological aspects can result in the intervention backfiring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841416</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841416</link>
        <title><![CDATA[Attention-guided hybrid CNN–transformer framework for cross-modality COVID-19 detection and lesion localization in CT and chest X-ray images]]></title>
        <pubdate>2026-06-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manas Gautam</author><author>P. Jagalingam</author>
        <description><![CDATA[Accurate and interpretable analysis of thoracic medical images is essential for reliable COVID-19 diagnosis; however, existing approaches face significant challenges in jointly performing classification and lesion localization across multiple imaging modalities. Suboptimal diagnostic knowledge and limited spatial awareness are the results of conventional deep learning approaches, especially models based on convolutional neural networks (CNNs). These methods mainly concentrate on local feature extraction and often handle classification and segmentation as independent tasks. Although transformer-based models enhance global contextual learning, their practical use in real-world clinical settings is limited due to their high computing resource requirements and large-scale dataset requirements. On top of that, there are currently no explainability procedures built into hybrid CNN-Transformer methods, and most of them use shallow or one-directional fusion algorithms. In order to overcome these shortcomings, this research suggests a CNN-Transformer hybrid attention-guided paradigm for COVID-19 identification and lesion localization across modalities utilizing separate CT and CXR datasets. The suggested design incorporates a ResNet-18 backbone for local feature extraction, a light Vision Transformer for global contextual modelling, a bidirectional cross attention fusion module for effective interaction between CNN and transformer representations, a feature refinement mechanism based on convolutional block attention module (CBAM), and a unified multi-task learning framework. In the CT data set, the experimental results prove that the proposed model is superior to the other models, having an accuracy of 98.29%, a precision of 98.14%, a recall of 98.13%, and an F1-Score of 98.11%. The model also has a Dice score of 0.847 and an Intersection-over-Union score of 0.848, respectively. The proposed model gets an accuracy of 97.74%, a precision of 97.93%, a recall of 97.52%, and an F1-Score. By enhancing generalizability and clinical reliability across imaging modalities, our findings demonstrate that the suggested paradigm delivers accurate, robust, and interpretable COVID-19 diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1811944</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1811944</link>
        <title><![CDATA[Vitality assurance in microservice architectures: introducing the health box testing methodology]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yusuf Köse</author><author>Sin-Chun Ng</author>
        <description><![CDATA[IntroductionMicroservice architectures introduce complex testing challenges, particularly for grey failures — partial degradation scenarios in which infrastructure signals appear normal while user-facing journeys fail silently. Existing monitoring approaches lack the sensitivity to surface such failures early.MethodsWe introduce Health Box Testing (HBT), a runtime vitality assurance methodology that deploys synthetic Health Agents to execute representative user journeys against production-like environments. A proof-of-concept evaluation was conducted across 12 controlled grey-failure injection trials using Playwright-based agents in a dedicated test environment.ResultsHBT detected all 12 injected grey-failure trials (coverage 100%; median MTTD = 70 s, IQR 48.5-86.8 s). The baseline monitoring approach detected 7 of 12 trials (58.3%), with a conditional median MTTD of 397 s (IQR 344.0-404.5 s). A Wilcoxon signed-rank test on the 7 paired trials confirmed HBT was significantly faster (W = 0, p = 0.01562, two-sided), with a median paired reduction of 322 s.DiscussionHealth Box Testing provides a complementary, toolchain-agnostic framework for continuous vitality assurance that surfaces user-impacting failures earlier than conventional infrastructure monitoring, particularly for grey failures invisible to standard telemetry. Future work includes automated root-cause classification and integration with cognitive-load-aware alerting frameworks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1853493</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1853493</link>
        <title><![CDATA[Micro-credentials in higher education: transforming credentialing for lifelong learning and workforce alignment]]></title>
        <pubdate>2026-06-05T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Xiang Wenjing</author><author>Hana Marešová</author><author>Shamshad Khan</author>
        <description><![CDATA[Micro-credentials (MCs) and digital badges (DBs) are transforming the world of higher education (HE) by providing a modular, stackable, and competency-based pathway of learning that is not confined to the traditional degree programs. The present paper synthesizes the current literature on MCs and DBs, including their design, adoption, and implementation, as well as future trends, institutional benefits, challenges, and governance concerns. The current research generally points to positive links to lifelong learning and workforce relevance when combined with flexible online platforms and personalized presentation. But the evidence to date is mostly cross-sectional and descriptive, with a lack of longitudinal evidence to support long-term and causal effects. Critical issues remain around inconsistent employer recognition, quality assurance, equity, and the absence of global standards, which could affect their efficiency, effectiveness, and scalability. This article finds that new technologies, such as blockchain-based credentialing, can support verification, portability, and trust, and that stakeholder collaboration improves relevance and labor market alignment. Through integrating conceptual, empirical, and policy viewpoints, this paper has identified the opportunities of longitudinal studies, regional adaptation, and policy development. The MCs are positioned as disruptive solutions, which will result in learner empowerment, institutional change, and more inclusive and future-oriented HE systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1852475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1852475</link>
        <title><![CDATA[Toward a disruptive neuroepistemological pedagogy: a conceptual framework for STEM education]]></title>
        <pubdate>2026-06-04T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Julián Roa González</author><author>Celia Ortega-Gómez</author><author>Coral González-García</author><author>José Luis Díaz Palencia</author>
        <description><![CDATA[This article proposes Disruptive Neuroepistemological Pedagogy (DNEP) as a conceptual and design-oriented framework for STEM education. The central claim is that some STEM concepts should not be taught only as stable curricular content, but as products of scientific disruption: they emerged because previous explanations became insufficient, because anomalies required new models, or because their use transformed society. DNEP integrates three dimensions: epistemological disruption, neuroeducational meaningfulness, and ethical-social responsibility. Its novelty does not lie in adding neuroscience, epistemology, or critical pedagogy as independent domains, but in coordinating them around disruptive STEM concepts that require conceptual revision, model-based reasoning, and responsible use. The article follows a conceptual and integrative review methodology. It defines the research gap, presents the core principles of DNEP, specifies design criteria, proposes a partial instructional model, and identifies boundary conditions for implementation. The framework is positioned against behaviorism, cognitivism, constructivism, holistic-humanistic education, and STE(A)M through an analytical comparison focused on learning, disruption, emotion, curriculum, teacher mediation, inquiry, and assessment. The article concludes that DNEP can contribute to STEM education by helping students understand why scientific ideas change, how such change can be learned meaningfully, and why scientific knowledge requires ethical and social responsibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834420</link>
        <title><![CDATA[Enhancing the coding experience in primary school with pepper: learning gains, cognitive workload, and student engagement]]></title>
        <pubdate>2026-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Janika Leoste</author><author>Alessandro Pagano</author><author>Veronica Rossano</author><author>Francesca Pia Travisani</author>
        <description><![CDATA[IntroductionThis study examines a Pepper-based coding activity in primary education through three complementary dimensions: changes in coding performance, perceived cognitive workload, and pupils' post-activity engagement responses. Rather than testing the causal effectiveness of Pepper in isolation, the study investigates how a robot-mediated coding activity can be implemented and evaluated in an authentic classroom context.MethodsTwenty-nine third-grade pupils (8–9 years old) from an Italian primary school participated in a teacher-orchestrated coding activity in which they programmed the Pepper robot to navigate a floor grid. Coding performance was assessed using curriculum-aligned pre- and post-tests scored on a 0–10 scale. The perceived workload was measured with a child-adapted NASA Task Load Index (NASA-TLX) questionnaire. Post-activity engagement responses were collected using an adapted version of the User Engagement Scale-Short Form (UES-SF), with particular caution in interpretation due to the limited internal consistency observed for several subscales.ResultsThe coding scores increased significantly from the pre-test (M = 5.83, SD = 3.09) to the post-test (M = 7.97, SD = 2.54), t(28) = 3.75, p < 0.001, with a size of the effect size of medium-to-large paired-samples (dz = 0.70). NASA-TLX responses indicated low mental and temporal demand, moderate physical demand, high perceived performance, and low frustration, suggesting that the activity was perceived as manageable by the pupils. The adapted engagement questionnaire provided limited descriptive evidence of positive post-activity responses, especially on the Reward subscale, while the other UES-SF dimensions were interpreted cautiously due to weak reliability.DiscussionThe findings provide preliminary classroom-based evidence that a Pepper-mediated coding activity was associated with increased coding scores, manageable perceived workload, and positive descriptive indications of perceived reward. However, because of the single-group pre–post design, small sample size, unequal pre- and post-test formats, short-term scope, and limitations of the adapted engagement measure, the results should not be interpreted as causal or generalizable evidence of Pepper's effectiveness. Instead, the study offers methodological and design insights for future comparative research on robot-mediated coding activities in primary education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1815738</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1815738</link>
        <title><![CDATA[An integrated model for digital student profiling based on multisource educational data]]></title>
        <pubdate>2026-06-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gulzhan Soltan</author><author>Assel Smaiyl</author><author>Gulnaz Zunimova</author><author>Mira Rakhimzhanova</author>
        <description><![CDATA[IntroductionThis paper proposes an integrated model of student digital profiling based on multisource educational data. The aim of the model is to improve the accuracy of predicting academic performance and learning risks. Unlike traditional approaches that rely on a limited set of academic indicators, the proposed model integrates academic, research, social, and behavioral characteristics to form a holistic representation of learning activity.MethodsThe methodological framework includes semantic clustering of disciplines using NLP methods, calculation of an integrated grade point average (IGPA), and the application of multimodal machine learning models. SHAP analysis was used to interpret the results. Experimental validation was conducted on real data from a university information system using the AUC-PR, LogLoss, KS, and PSI metrics.ResultsThe results demonstrate that the use of multimodal data increases the accuracy of learning risk prediction by 8–12% compared with baseline models. Dynamic behavioral features enable the early identification of academic underperformance risks at initial stages of study. The model demonstrated robustness when applied across different faculties and scalability under conditions of increasing data volume.DiscussionPractical implementation in the form of interactive dashboards confirmed the applicability of the proposed approach for supporting pedagogical and managerial decision-making. The model provides a foundation for personalized academic support and the development of digital educational analytics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753080</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753080</link>
        <title><![CDATA[Hardware- and software-based fingerprint liveness detection schemes—A literature review]]></title>
        <pubdate>2026-06-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Rubab Mehboob</author><author>Hassan Dawood</author><author>Ali Daud</author><author>Hussain Dawood</author>
        <description><![CDATA[Technological advancements have shaped the methods for ensuring personal safety and safeguarding digital assets. In this regard, biometric systems are innovative technological solutions that utilize various behavioral and physiological traits for personal verification and identification. This technology provides efficient and seamless authentication while improving users' convenience in a range of security applications, including border security, physical access control, healthcare, and financial transactions. Fingerprint-based verification systems are vulnerable to various attacks, including spoofing, presentation attacks, and latent fingerprint attacks. Henceforth, it is essential to incorporate a robust and reliable liveness detection mechanism into the authentication system to enhance the security of such systems. Since fingerprint liveness detection has been an active area of research, many techniques have been proposed for handling this problem by extracting different characteristics such as minutiae, ridges, texture, pores, perspiration, and morphological features. In this article, the state-of-the-art liveness detection schemes along with their taxonomy from 2003 to 2025 are chronologically analyzed. In this article, we have presented the techniques, their parameters, their pros and cons, and the evaluation matrices. Moreover, this article inculcates a comprehensive survey of the various hardware-, software-based approaches focusing on the deep learning liveness detection techniques. We have also investigated the standard benchmarks of the LivDet series that are publicly available and the evaluation protocols.]]></description>
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