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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>2026-07-31T23:17:32.722+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1838941</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1838941</link>
        <title><![CDATA[Semantic de-identification of burned-in PHI in DICOM medical images: a deep learning–NLP pipeline validated on clinical and phantom TMM datasets]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Remya Sethulekshmi</author><author>Manu J. Pillai</author><author>Nihal Ahammed</author><author>Somula Ramasubbareddy</author><author>Yong Yun Cho</author>
        <description><![CDATA[IntroductionThe growing adoption of AI-based healthcare research has increased the need for properly anonymized medical imaging datasets. PHI within DICOM files - particularly burned-in pixel-level text - poses significant privacy and regulatory risks. Existing methods either focus solely on metadata or remove all detected text indiscriminately, sacrificing clinically relevant annotations.MethodsThis paper proposes a semantic de-identification pipeline integrating YOLOv11n-based text detection, domain-optimized EasyOCR, and a hybrid natural language processing (NLP) classification module combining regular expressions, keyword matching, and named entity recognition. A dual-path architecture processes metadata and pixel-level PHI in parallel, enabling complete DICOM sanitization while preserving non-PHI clinical annotations. The system was evaluated on 1,042 multi-modality DICOM images (CT, MRI, X-ray, ultrasound). As a secondary evaluation, the pipeline was also applied to two tissue-mimicking material (TMM) phantom datasets from TCIA - the RIDER Phantom MRI and Phantom FDA CT (RIDER = Reference Image Database to Evaluate Therapy Response; FDA = Food and Drug Administration) - which served as surrogates for controlled evaluation of metadata and burned-in identifier removal.ResultsThe system achieves an F1-score of 95.4%, 96.1% recall, a structural similarity index measure (SSIM) of 0.969, a peak signal-to-noise ratio (PSNR) of 28.9 dB, and processes each image in 2.8 s. It achieves SSIM of 0.986 and PSNR of 49.0 dB on RIDER Phantom MRI, and SSIM of 0.974 and PSNR of 31.5 dB on Phantom FDA CT.DiscussionThese results confirm that the pipeline preserves quantitative pixel fidelity when applied to institutional and device identifiers embedded in phantom acquisitions, supporting blinding for domain-generalization studies across institutions. The modular design supports institutional customisation, making it suitable for clinical research workflows and privacy-compliant phantom imaging pipelines.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1829545</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1829545</link>
        <title><![CDATA[When language feels like recognition: Parasocial Mattering Resonance as a cognitive vulnerability in human–LLM interaction]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Gillian W. Oakenfull</author>
        <description><![CDATA[Large language models generate language that is individually responsive, contextually specific, and continuously available, properties that make such language the most precise and sustained mattering-frequency signal that mediated language has yet produced. This paper argues that for individuals experiencing mattering depletion, the chronic condition in which one or more fundamental dimensions of relational significance are insufficiently fulfilled, this signal produces a predictable and consequential cognitive response. We introduce Parasocial Mattering Resonance (PMR): a cognitive processing state in which the linguistic features of LLM-generated language activate significance-seeking schemas in mattering-depleted individuals, generating a genuine subjective experience of recognition, connection, and mattering provision. This experience is psychologically real on the individual’s side but has no reciprocal reality in the system producing it. Drawing on schema accessibility theory, dual-process models of social cognition, and a five-dimension framework of mattering as structured human need, we develop a five-step mechanism connecting mattering depletion to PMR’s behavioral consequences, including increased compliance, reduced skepticism, extended engagement, and displaced relational investment. Six formal propositions specify the mechanism, its boundaries, and its heterogeneous distribution across depletion profiles. We establish that PMR is independent of system design intent, that source disclosure is theoretically insufficient to prevent PMR among the most vulnerable populations, and that this vulnerability is not randomly distributed; it concentrates in already-marginalized populations whose structural, organizational, and relational environments have produced the deepest depletion. For the most isolated individuals, PMR may also constitute a temporary psychological resource, making the distinction between resonance as relief and resonance as displacement a theoretically and clinically consequential one. Implications for cognitive theory, LLM design, and equity are developed alongside an empirical agenda for testing the propositions advanced.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855830</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1855830</link>
        <title><![CDATA[Exploring the interplay between voice, personality, and gender in human-agent interactions]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kai Alexander Hackney</author><author>Lucas Guarenti Zangari</author><author>Jhonathan Sora-Cardenas</author><author>Emmanuel Munoz</author><author>Sterling R. Kalogeras</author><author>Betsy DiSalvo</author><author>Pedro Guillermo Feijóo-García</author>
        <description><![CDATA[IntroductionTo foster effective human-agent interactions, designers must understand how vocal cues influence the perception of agent personality and the role of user-agent alignment in shaping these perceptions. In this work, we examine whether users can perceive extroversion in voice-only artificial agents and how perceived personality relates to user-agent synchrony.MethodsWe conducted an exploratory study with 388 participants, who evaluated four synthetic voices derived from human recordings, varying by gender (male, female) and personality expression (introverted, extroverted).ResultsOur findings suggest that participants were able to differentiate perceived extroversion across the female voice conditions, but not consistently across the male voice conditions. We also observed preliminary evidence consistent with perceived personality synchrony, particularly in participants' evaluations of the first agent encountered. These findings represent an initial step toward understanding personality synchrony in voice-based human-agent interactions. Because each experimental condition was represented by a single synthesized voice and no objective acoustic validation of the intended personality manipulation was conducted, these findings should be interpreted within the exploratory scope of this study.DiscussionWe discuss the implications of these findings while considering the limitations of stimulus diversity and voice representation, and outline implications for the design of voice-based agents, particularly regarding the interaction between gender, personality perception, and initial user impressions. This paper contributes exploratory evidence and methodological insights for investigating the interplay of user-agent personality and gender synchrony in the design of human-agent interactions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1785527</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1785527</link>
        <title><![CDATA[ResFL-UAV++ for robust and resource-constrained secure federated learning with adversarial defense in UAV systems]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. Sophia</author><author>P. Getzi Jeba Leelipushpam</author><author>T. Jemima Jebaseeli</author>
        <description><![CDATA[Federated Learning (FL) enables privacy-preserving collaborative model training for Unmanned Aerial Vehicles (UAVs). However, its decentralized nature makes it vulnerable to adversarial attacks such as model poisoning, label flipping, and backdoor attacks. To address these challenges in resource-constrained UAV environments, this study proposes ResFL-UAV++, a lightweight and secure FL framework incorporating a multi-layer defense mechanism. The framework integrates a multi-metric anomaly detection module based on cosine similarity, L2-Norm Filtering, and Temporal Update Consistency (TUC) to identify malicious UAV updates. A hybrid robust aggregation strategy combining Trimmed Mean and Krum mitigates adversarial effects while preserving model convergence. Additionally, adversarial training using the Fast Gradient Sign Method (FGSM), together with Differential Privacy (DP), enhances reliability and data confidentiality while incurring minimal computational overhead. Experimental evaluation on the HIT-UAV Infrared Thermal dataset and the WebUAV-3M demonstrates that ResFL-UAV++ achieves 98% accuracy under adversarial conditions. The framework reduces the Backdoor Attack Success Rate (ASR) to below 20%. Furthermore, it achieves over 98% accuracy in adversarial UAV detection while introducing less than 4% system overhead. These results demonstrate the effectiveness and practicality of ResFL-UAV++ for secure FL in UAV environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1939656</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1939656</link>
        <title><![CDATA[Correction: FBR-PAEKS: revocable public-key authenticated keyword search with forward privacy for dynamic cloud environments]]></title>
        <pubdate>2026-07-28T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Mishal Ismaeel</author><author>Ali Raza</author>
        <description></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847376</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1847376</link>
        <title><![CDATA[Next-generation intrusion detection in cyber-physical systems using an ensemble of quantum-inspired and deep neural models]]></title>
        <pubdate>2026-07-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maloth Sagar</author><author>Vanmathi C</author>
        <description><![CDATA[Cyber-physical systems (CPSs) could cause actuation and safety risks. Intrusion detection is essential for preserving the system's integrity due to growing security issues. Nowadays, deep learning (DL) schemes have been deployed to enhance the detection of cyber-attacks, yet these models are prone to overfitting, which reduces detection performance. Hence, this research proposes a novel deep learning-based Intrusion Detection System (IDS) for CPS to address these limitations. The proposed methodology consists of four key stages, including preprocessing, feature extraction, feature selection, and intrusion detection. Data preprocessing is performed via cleansing, followed by the extraction of statistical [mean, median, and standard deviation (SD)], entropy-based, improved correlation, improved mutual information (MI), flow-based, and Improved Information Gain (IIG) features, which are derived to obtain the important features. The Archimedes Algorithm with Team Work Principle (AA_TWP), integrating the Archimedes Optimization Algorithm (AOA) and the Teamwork Optimization Algorithm (TOA), with modifications to the exploration phase, is employed to efficiently select the most relevant features. The selected features, along with preprocessed data, are fed into an ensemble of Deep Belief Networks (DBNs), Quantum Deep Neural Networks (QDNNs), and optimized Bidirectional Long Short-Term Memory (Bi-LSTM), with Bi-LSTM weights further tuned by AA_TWP. The ensemble outputs are averaged to produce the final intrusion decision. Experimental results demonstrate 91.52% accuracy and 91.76% Matthews Correlation coefficient (MCC), highlighting the effectiveness of the proposed approach, which outperforms existing techniques.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1877426</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1877426</link>
        <title><![CDATA[AI-driven automation for CI/CD pipelines using attention-enabled reptile meta learning]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Graeson Joshua Elijah</author><author>Mythily M</author><author>Fahmid Al Farid</author><author>Neeraj Addura G R</author><author>Salaja Silas</author><author>Elijah Blessing Rajsingh</author><author>Jia Uddin</author><author>Hezerul Bin Abdul Karim</author>
        <description><![CDATA[Healthcare systems increasingly rely on software to support critical operations such as patient monitoring, diagnostic decision-making, and real-time data management, making reliability, speed, and continuous availability essential. The software development and deployment phases, therefore, play a crucial role in ensuring that the healthcare applications remain stable and compliant with regulatory standards. However, traditional Continuous Integration/Continuous Deployment (CI/CD) pipelines are largely reactive, relying on manual testing, delayed feedback, and post-failure recovery mechanisms, which are time-consuming and prone to errors. Such limitations lead to deployment instability, inefficient resource utilization, and increased operational risk in mission-critical healthcare environments. To overcome these challenges, automated and intelligent approaches are required to enable proactive failure detection, faster recovery, and reliable deployment. In this regard, a novel AI-driven self-adaptive framework, Continuous Integration/Continuous Deployment Attention-enabled Reptile Meta-Learning (CICD-ARML), is proposed. The proposed system continuously learns from pipeline telemetry and adapts to dynamic deployment conditions, reducing dependency on manual intervention. Experimental results demonstrate that CICD-ARML achieves a 96.8% build success rate, reduces mean recovery time by 35%, reduces deployment risk by 59%, and achieves 94% prediction accuracy. These findings highlight the effectiveness of the proposed framework in enhancing the reliability, efficiency, and adaptability of healthcare software deployment systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848687</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1848687</link>
        <title><![CDATA[A Hybrid Quantum-Resistant Resumption framework for zero-RTT TLS 1.3]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tahera Begum Abdul</author><author>Venkata Ramana Kondapalli</author>
        <description><![CDATA[IntroductionZero-round-trip-time (0-RTT) resumption is a central performance feature of TLS 1.3, cutting reconnection latency by letting clients attach early application data to pre-shared keys derived from previously issued session tickets. Latency-sensitive deployments—web services, API gateways, IoT fleets-rely heavily on this mechanism. However, today's session tickets derive their symmetric keys from a purely classical X25519 exchange, leaving them exposed to Harvest-Now-Decrypt-Later (HNDL) attacks: an adversary who records the ticket exchange today gains the ability to decrypt all associated 0-RTT early data retroactively once a cryptographically capable quantum machine exists. This paper introduces HQRT (Hybrid Quantum-Resistant Resumption for TLS 1.3), which folds a hybrid X25519 + ML-KEM-768 encapsulation directly into the NewSessionTicket lifecycle, rendering tickets quantum-safe without adding any extra round trip.MethodsAt the core of HQRT is a Hybrid Resumption Master Secret, constructed jointly from classical and post-quantum shared secrets and integrated into the existing TLS 1.3 key schedule as a drop-in addition. The paper supports this construction with a formal, game-based security model proving HNDL resistance, and extends the replay-protection analysis to quantum adversaries. A working proof-of-concept was implemented on OpenSSL 3.x via the OQS provider, with benchmarking conducted across server, desktop, and IoT hardware.ResultsThe latency penalty introduced by HQRT is just 4–9%, with a throughput loss of 6.5% relative to classical 0-RTT-a stark contrast to the 81–89% overhead imposed by full post-quantum handshakes. Across multi-session workloads, the amortised overhead falls by 34–97%, and latency-distribution tails diverge from the classical baseline by under a millisecond.DiscussionThese results demonstrate that HQRT offers a deployable, incremental route to quantum-safe TLS resumption, delivering HNDL resistance and quantum-safe replay protection at a fraction of the cost of full post-quantum handshakes, while leaving existing certificate infrastructure untouched.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1906653</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1906653</link>
        <title><![CDATA[Editorial: Embodied perspectives on sound and music AI]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Çağrı Erdem</author><author>Anna Xambó Sedó</author><author>Stefania Serafin</author><author>Carsten Griwodz</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1807842</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1807842</link>
        <title><![CDATA[Machine learning-based sentiment analysis to determine social perception of Generation Z on Twitter/X]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hugo Vega-Huerta</author><author>Luis Casaperalta-Pacheco</author><author>Frida López-Córdova</author><author>Adegundo Camara-Figueroa</author><author>Rubén Gil-Calvo</author><author>William Enriquez</author><author>Juan Carlos Lázaro-Guillermo</author><author>Jessy Isabel Vargas Flores</author><author>Mario Chauca</author><author>Jose Luis Cuya-Camara</author><author>Ivan Adrianzén-Olano</author><author>Katherin Vanessa Rodriguez-Zevallos</author>
        <description><![CDATA[The arrival of Generation Z in the social and work environment has sparked a heated debate on social media, marked by a division between innovative visions and critical stereotypes. This research develops a sentiment analysis model based on machine learning to understand public perception of this population group. Using a dataset obtained from Twitter/X through content and data extraction from the web with the Octoparse tool, three classification algorithms were trained and evaluated: Support Vector Machines (SVM), Random Forest, and Long Short-Term Memory (LSTM). Due to the class imbalance inherent in generational discussions, data balancing techniques (SMOTE, ADASYN) were applied. The results indicate that the Optuna-optimized LSTM model with SMOTE+Tomek balancing performed best with an accuracy of 82.39%, outperforming classical approaches. This tool allows for the identification of opinion trends (positive, negative, or neutral) about Generation Z, providing valuable input for sociologists and organizations seeking to understand the dynamics of “Generation Z” or “Centennials.”]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830238</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830238</link>
        <title><![CDATA[Beyond the screen: craft as inquiry in human-computer interaction]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nidhi Singh Rathore</author><author>Ariana Mayeri</author><author>Tamara Rushby</author>
        <description><![CDATA[The field of human-computer interaction (HCI) has been historically shaped by efficiency, productivity, and task completion—priorities that reflect the class and gender biases of those who built it. We propose critical crafting as a framework for disrupting these hierarchies and reimagining what HCI can be. Using two research-through-design case studies, crocheting and embroidery serve as modes of inquiry that point toward a future of HCI that goes beyond screens and may imagine delight, joy, and surprise through crafting technology. Developed over 8 months within the Interaction Design Thesis Course at the Corcoran School of the Arts & Design at The George Washington University, these case studies pair a distinct craft technique with a technological intervention: the first translates the embodied, repetitive logic of crochet into an interactive digital installation designed to invite conditions for joy and presence; the second embeds responsive technology into an embroidery artifact to transform it from a static object into a two-way communicative system that invites touch. Together they propose how technology can function as both vessel and mediator for craft-based experience. These findings suggest that centering craft in technological development legitimizes embodied, process-oriented ways of knowing that HCI has historically overlooked—and that the stakes of that overlooking are not merely aesthetic, but political. Critical crafting offers a reorientation of HCI towards human experience and emotions, arguing that craft offers an important route for expanding HCI beyond dominant screen-based paradigms, toward interfaces that foster conditions for joy, play, and curiosity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1712509</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1712509</link>
        <title><![CDATA[Jewish entrepreneurial labor TikTok: navigating visibility, identity, and algorithmic harm]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tom Divon</author><author>Gabrielle D. Beacken</author><author>Jess Rauchberg</author><author>Jessica Maddox</author>
        <description><![CDATA[Identity work has become a marketable asset in the platform economy—one that must align with both audience expectations and platform logics to achieve visibility. Yet recognition-enabling systems also expose creators to harassment and suppression. To examine these dynamics, this study examines #JewTok, a TikTok enclave dedicated to mobilizing Jewish identity as a form of entrepreneurial labor shaped by tension and precarity. Drawing on ethnographic observation of 27 accounts and interviews with 12 creators, we identify two central entrepreneurial practices: (1) Jewish ambassadorship, where creators engage in cultural brokerage, community building, religious exploration, and justice advocacy to cultivate solidarity, translate traditions, and mobilize visibility; and (2) Jewish hardships, where creators confront algorithmic antisemitism while their content is subjected to surveillance dynamics that render Jewish life hypervisible, and stripped of context. The findings show that creative labor is especially precarious when tied to ethnoreligious identity—amplifying racialized hierarchies and rendering some voices increasingly (in)visible. We argue that creators navigate these volatile conditions in their identity work due to platform design and governance structures, which in turn transform identity into a precarious commodity sustained through entrepreneurial labor.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1826732</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1826732</link>
        <title><![CDATA[AI-integrated interactive learning system for enhancing cybersecurity education]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yahya Khalfan Habib Al Foori</author><author>Solomon Sunday Oyelere</author>
        <description><![CDATA[Cybersecurity education often struggles to provide scalable, practice-oriented learning experiences while maintaining pedagogical oversight. This paper presents AI-CES, a lecturer-in-the-loop, AI-assisted interactive learning platform designed to support scenario-based cybersecurity education. The system combines AI-generated simulation drafting, lecturer review and approval, student-facing multi-round simulations, and performance tracking within a single web-based environment. We report the design and preliminary evaluation of the prototype rather than a validated effectiveness study. An exploratory evaluation was conducted with 30 participants from one computer science department, including 10 lecturers and 20 students, using role-specific post-use surveys with Likert-scale and open-ended items. Descriptive statistics and thematic coding were used to analyse perceived usability, engagement, content quality, and educational value. Participants generally viewed the platform positively, particularly its interactivity, immediacy of feedback, and lecturer oversight of AI-generated content. Lecturers also valued the visibility provided by the analytics dashboard. However, the study does not establish causal effects on learning outcomes, teaching effectiveness, or long-term skill development. Findings should, therefore, be interpreted as evidence of feasibility and user acceptability in a pilot setting. Limitations include the small single-site sample, reliance on self-reported perceptions, and dependence on third-party AI services. Future work should incorporate controlled comparisons, pre/post knowledge measures, multi-institution evaluation, and longitudinal analysis of learner interaction data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1823366</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1823366</link>
        <title><![CDATA[Reimagining infrastructure for video game accessibility: exploring Access Profiles with players with disabilities and game designer-developers]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lloyd May</author><author>Dan-Ha Le</author><author>Graham Lazorchak</author><author>Emily Q. Wang</author>
        <description><![CDATA[Games provide opportunities for entertainment, socializing, and community building. However, many games are not built with players with disabilities (PwDs) in mind. Accessibility features, like captions or auto-aim, vary in availability and quality. While some standards and guidelines exist, designer-developers continue to face several resource, knowledge, and practical challenges in implementing accessibility features. Complementing prior work about specific needs and features, we present Access Profiles (AP), a framework for multi-directional communication between game designer-developers and PwDs. Through a qualitative study, we investigated how systems utilizing the AP framework could support designer-developers in selecting, implementing, and documenting accessibility-related features in their games, as well as how PwDs may be supported in both finding accessible games and improving the initial experience of new games. Our findings include insights on how AP has the potential to improve game discovery and configuration, but also necessitate comprehensive changes across game engines, retailers, and launchers used across the gaming ecosystem. We then apply theories of propagation to discuss how AP can support the standardization of known features and spreading of novel features to systematically improve game accessibility over time.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824053</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824053</link>
        <title><![CDATA[Use of the human-centered experience approach to identify unpleasant emotions in a curriculum analysis process]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Juan Manuel Galindo-Medina</author><author>Rafaela Blanca Silva-López</author><author>Román Anselmo Mora-Gutiérrez</author><author>Oswaldo Sánchez-Andrade</author>
        <description><![CDATA[This article presents two exercises conducted within research during a process of reviewing engineering undergraduate degree programs. The objective is generating a curriculum evaluating model supported by information visualization tools. These exercises where 1. Generating the state-of-the-art in regard of curriculum evaluation and 2. Assessing the variation of evaluators emotions through the curriculum reviewing process. The state-of-the-art was carried through five phases using bibliographic and bibliometric analysis. Tools like ResearchRabbit and Gephi were utilized on the journals analysis. Results revealed a lack of research focused on the human experience or supported by information visualization tools. Based on the state-of-the-art results, a quasi-experiment was conducted for analyzing feelings during the review of undergraduate degree programs. Qualitative data was collected via a questionnaire based on Peter Desmet’s circle of emotions and the User Experience Questionnaire by Hinderks, Schrepp, and Thomaschewski. Data was collected during three different stages of the process. The information—processed via information visualization charts—showed that events such as sudden changes in the delivery times of results or in objectives and goals can provoke unpleasant feelings and emotions. The results suggest the need to implement strategies to minimize such situations. As results of both exercises conclude, the creation of a model for curriculum evaluation supported on information visualization tools is pertinent. Nevertheless, a new layer or stage should be from the human-experience approach. Opportunities for derivate research on unpleasant emotions impact on curriculum evaluation and emotions data processing via information visualization where identified.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1851687</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1851687</link>
        <title><![CDATA[The adoption gap in adaptive learning path generation: an analytical review of barriers and operational requirements]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Aline Dobrovsky</author><author>Marko Hofmann</author><author>Uwe M. Borghoff</author>
        <description><![CDATA[IntroductionAdaptive learning path generation (ALPG) aims to personalize the selection and sequencing of learning activities based on learner state and instructional goals. Despite decades of research and multiple methodological waves, ranging from intelligent tutoring systems (ITS) and metaheuristic optimization to reinforcement learning and large language model (LLM)-based approaches, its adoption in operational learning environments remains limited.MethodsThis paper employs an iterative literature review design combining an umbrella review of existing surveys with a supplementary systematic search on adoption, barriers, and operational conditions. The findings are synthesized through qualitative thematic aggregation to identify recurring barriers, derive adoption-critical requirements, and develop a requirement-based analytical framework for assessing ALPG methods.ResultsThe analysis reveals a persistent research-practice gap. Most ALPG approaches remain at the level of prototypes, course-level implementations, or domain-specific systems, while mainstream platforms rely on rule-based personalization. The literature identifies interdependent barriers across algorithmic, data-related, pedagogical, technical, organizational, human, and ethical dimensions. These barriers can be reframed as operational requirements, including scalability, robust learner modeling, pedagogical grounding, system integration, governance compliance, and human-centered design.DiscussionThe findings indicate that ALPG is not only a technical sequencing problem but a sociotechnical orchestration problem. The paper contributes a requirement-based framework that links methodological design choices to adoption-relevant conditions and enables a structured assessment of practical feasibility. This perspective supports the analysis of which methodological families can operate under real-world constraints and highlights directions for future research toward deployment-ready adaptive learning systems that support advanced adaptive learning path generation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1882606</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1882606</link>
        <title><![CDATA[Road use as conversation: a Multimodal Multi-scale Coordinated Action Model (MCAM) for immersive and generative traffic safety applications]]></title>
        <pubdate>2026-07-17T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Merve Keskin</author><author>Rebekah Wegener</author><author>Martin Karl Moser</author>
        <description><![CDATA[Understanding how VRUs—pedestrians, cyclists, and micromobility users—coordinate social action and resolve conflict in mixed-mode urban traffic is not merely a technological but a fundamental human factors problem. The challenge concerns how people perceive, attend to, and act upon the behavioral signals of others under real-world conditions of unpredictability and uncertainty. Intention prediction is a key mechanism through which traffic safety applications attempt to manage this complexity, yet existing frameworks treat coordination as a binary classification task and fail to capture its multi-scale, gradient nature. Current research has major gaps related to data, robustness, and contextual understanding. XR and digital twin systems are deployed for urban traffic safety, yet a critical validity problem persists: these systems are designed and evaluated without ground-truth knowledge of how people look, move, and experience cognitive load in real mixed-mode traffic. Simulator-based studies offer control but sacrifice ecological validity; existing real-world datasets are vehicle-centric, single-modality, and restricted to pedestrian-crossing scenarios. This paper introduces the MCAM, a theoretical framework that reconceptualises urban mobility as structured communicative interaction governed by shared behavioral conventions across three spatiotemporal scales—micro (1–5 s), meso (5–30 s), and macro (longer-term contextual)—operationalising conflict as a five-phase gradient progression from coordinated flow to conflict event. MCAM is operationalised through a multimodal field study using a BIBD at three ecologically distinct urban sites (a signalized intersection, a shared space, and a cycling corridor), observed across peak hours and seasonal conditions. Thirty gender- and age-balanced participants each navigate all three sites as pedestrians, cyclists, and e-scooter riders—a within-subject cross-modal design is often absent from the majority of existing intention detection datasets— would give approximately 60 h of usable recording and 1,500 hours of annotation. Three contributions are presented: (1) a reusable multimodal field methodology for ecologically valid human factors data collection; (2) an MCAM-grounded annotation scheme applicable to both real and simulated environments; and (3) a methodological bridge between real-world human behavior and the design of immersive and generative traffic safety applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1867907</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1867907</link>
        <title><![CDATA[Fine-grained continuous user authentication via mouse grip pressure biometrics]]></title>
        <pubdate>2026-07-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiang Zou</author><author>Jingbo Wang</author><author>Yanqiu Liu</author><author>Jiali Wu</author><author>Guangjun Liu</author>
        <description><![CDATA[Computers have become indispensable tools in modern society and they often serve as repositories for large amounts of private and confidential information in public. User authentication is therefore a fundamental mechanism for protecting device security and user privacy. Traditional authentication methods such as passwords are not tightly bound to user identity and impose a significant memory burden, which has motivated the widespread adoption of biometric-based authentication approaches, such as fingerprint and facial recognition. However, fingerprint- and face-based authentication typically requires additional hardware support and can be vulnerable to interception by ultra-high-definition cameras, raising serious privacy concerns. To solve these problems, this paper proposes a fine-grained and continuous user authentication method based on mouse grip pressure biometrics. By deploying resistive pressure sensors on the surface of a mouse, we capture individualized pressure distribution patterns generated during natural mouse gripping. To further enhance discriminative capability, we refine the sensor contact layout to improve spatial feature resolution. Moreover, we introduce a layered representation for omnidirectional pressure signals to mitigate sensitivity to grip direction variations. Extensive experimental results demonstrate the effectiveness of the proposed system, achieving the FAR below 0.1% and the FRR below 0.4%. These results indicate that mouse grip pressure constitutes a highly discriminative and robust biometric modality for continuous user authentication.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892734</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1892734</link>
        <title><![CDATA[Truthfulness-constrained generative AI for job applications: a framework for ethical candidate empowerment in digital labor markets]]></title>
        <pubdate>2026-07-17T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Stephen Santhosh</author><author>Gabriela Jennifer</author>
        <description><![CDATA[Generative artificial intelligence tools are increasingly used by job seekers to produce application materials, raising concerns about credential fabrication and its downstream effects on hiring ecosystem integrity. Employers have responded with rigid applicant tracking system (ATS) configurations that, according to recent estimates, filter out qualified candidates who do not exactly match automated criteria. We propose a theoretical model we call the fabrication–filtration spiral, in which candidate fabrication prompts stricter employer filtering, which in turn invites more fabrication. We offer this as a hypothesis, not a demonstrated effect. The consequences fall disproportionately on vulnerable populations: immigrants in visa-dependent job searches, career changers, and candidates from non-traditional backgrounds. In this Perspective, we propose that candidate-facing AI tools should be designed with explicit architectural truthfulness constraints rather than operating as unconstrained optimization engines. Drawing on a patent-pending system and empirical observations from a deployed job analysis platform, we describe a three-component framework: (1) a tiered guidance engine that advises candidates when not to apply, (2) a resume content generator constrained to reframe only verified experience, and (3) a narrative generator governed by a hard truthfulness rule prohibiting the invention of facts absent from the candidate's resume. We situate this framework alongside recent work on proportionality constraints in AI for psychosocial support, arguing that bounded optimization—where AI systems operate within explicit ethical limits—is a generalizable design principle for human-facing AI in high-stakes contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886719</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886719</link>
        <title><![CDATA[Real-time saliency-guided deep watermarking on Kria KV260: a Vitis AI accelerated proxy architecture]]></title>
        <pubdate>2026-07-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mehmet İrfan Gedik</author><author>Aysun Coşkun</author>
        <description><![CDATA[The rapid growth of Industrial Internet of Things (IIoT) ecosystems and autonomous surveillance networks has necessitated the shift of digital content security from central servers to the data-generating edge. In industrial security scenarios, operators must continuously monitor live video streams and optionally capture high-resolution, verifiable evidence snapshots. However, high computational costs and hardware-based precision losses prevent the real-time execution of deep learning-based watermarking models on resource-limited embedded devices. This study proposes a hardware-aware and semantically oriented real-time watermarking architecture running on the Xilinx Kria KV260 FPGA platform. The fundamental innovation of the proposed system is the asynchronous “Proxy Frame” software architecture, which allows heavy Convolutional Neural Networks (CNNs) to run in the background, isolated from the live video stream. Thus, highly secure watermarking can be performed at 1080p resolutions without compromising the fluidity of the 30 FPS live preview offered to the operator. Furthermore, a Noise-Assisted Dithering technique, inspired by stochastic resonance, was used to mitigate the “Signal Fading” problem arising from watermark signal loss during 8-bit integer (INT8) quantization on the Deep Learning Processing Unit (DPU). By injecting controlled Gaussian noise into the quantized inference pipeline, the detectability of subthreshold weak watermark signals was increased, reducing the hardware Bit Error Rate (BER) from 18.75% to 13.06%. MobileNetV2 and ResNet50-FCN based saliency models achieved an average PSNR visual quality of 39.86 dB by concealing the payload in perceptually insignificant regions. Under clean hardware conditions, the system achieved a baseline BER of 2.6% (with MobileNetV2). In attack tests, the BER remained below 15% for MobileNetV2 under most standard degradations, with the stated exceptions of severe JPEG compression and deliberate geometric cropping. This end-to-end hardware and software solution demonstrates that theoretical deep learning models can be integrated into industrial smart cameras without experiencing performance bottlenecks, particularly for static infrastructure monitoring.]]></description>
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