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        <title>Frontiers in Computer Science | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science</link>
        <description>RSS Feed for Frontiers in Computer Science | New and Recent Articles</description>
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        <pubDate>2026-10-03T19:56:35.825+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1934449</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1934449</link>
        <title><![CDATA[Efficient video violence detection through segmented temporal sampling and CNN–transformer modeling]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fray L. Becerra-Suarez</author><author>Karem Menacho-Navarrete</author><author>Mónica Díaz</author><author>Franccesca A. Recharte-Trucios</author><author>Felix R. Landeo-Castillo</author><author>Lloy Pinedo</author>
        <description><![CDATA[Detecting violence in video requires models capable of capturing spatial and temporal information without relying on long sequences, optical flow, or additional modalities. This study presents a CNN–Transformer framework based on segmented temporal sampling, evaluating 4, 8, and 16 frames per video. Each frame is encoded using EfficientNet-B0 pre-trained on ImageNet; features are projected onto 256-dimensional tokens, supplemented with sinusoidal positional encoding, and processed using a Transformer encoder followed by attention pooling. The framework was evaluated using video-level partitioning, multi-seed training, bootstrapping, robustness analysis, computational efficiency, independent benchmark evaluation and cross-dataset generalization. The 4-frame configuration achieved the most favorable multi-seed validation performance and was therefore selected for final evaluation. On the RLVS test set, the selected configuration achieved an Accuracy of 0.9508 ± 0.0038, F1-score of 0.9517 ± 0.0039, MCC of 0.9022 ± 0.0078, and PR-AUC of 0.9915 ± 0.0008. Under Gaussian noise, the CNN–Transformer showed the smallest MCC degradation (ΔMCC = −0.1853). Independent evaluation yielded Accuracies of 0.9517 ± 0.0076 on Hockey Fight Videos and 0.8857 ± 0.0143 on AIRTLab. The final model contained 5.39 million parameters and achieved 9.57 ± 1.25 ms inference latency per video. These results support segmented temporal sampling as an efficient strategy for violence detection while highlighting remaining challenges under domain shift.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1896307</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1896307</link>
        <title><![CDATA[ARAM: a threshold model for understanding occupant acceptance of architectural robot adaptations]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Alex Binh Vinh Duc Nguyen</author><author>Rania Christoforou</author><author>Ilaria Pigliautile</author><author>Veronica Martins Gnecco</author><author>Anja Pogladič</author><author>Sara Arko</author><author>Anna Laura Pisello</author><author>Davide Schaumann</author><author>Marcel Schweiker</author><author>Andrew Vande Moere</author>
        <description><![CDATA[Physical interior elements are increasingly becoming roboticised to support occupant needs. Partitions, ceilings, and furniture can now physically and autonomously change shape or move to architecturally “adapt” indoor spaces, with the aim of improving occupant comfort, satisfaction, and wellbeing. However, occupants sometimes reject these adaptations when they are perceived as disruptive, unpredictable, or difficult to control. Motivated by the goal of making architectural robots more acceptable, we involved an interdisciplinary team of experts in human-building interaction, human-robot interaction, psychology, anthropology, and human comfort to qualitatively re-analyze how occupants accepted adaptations across three previous studies on robotic partitions. We found that an occupant tended to accept an adaptation when they recognized a personal need that the architectural robot was capable of addressing in a socially justifiable way. Accordingly, we developed the Architectural Robot Acceptance Model (ARAM), in which three qualitative thresholds along a continuum of perceived stress severity define four distinct acceptance responses to a specific adaptation. As the positions of these thresholds may vary across occupants and situations, ARAM supports testable estimates of how an occupant may respond to different appraisal states of personal need, adaptation capability, and social justification. Because architectural robot adaptations may simultaneously be experienced as robotic behaviors, building automation, and socially situated environmental adjustments, ARAM is particularly relevant to individual adaptations that enter occupants' spatial experience and may affect co-located others. We then discuss how the model can inform situated estimates of occupant acceptance of architectural robot adaptations, and outline how its broader relevance to other robotic, automated, intelligent, and smart technologies embedded in the built environment may be evaluated in future work.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883472</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883472</link>
        <title><![CDATA[Technology Acceptance Model in video-sharing platforms: comparisons between TikTok and YouTube]]></title>
        <pubdate>2026-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Juhyung Sun</author><author>Sun Kyong Lee</author><author>Norman Wong</author>
        <description><![CDATA[IntroductionOnline video-sharing platforms – TikTok and YouTube – have become central to young adults’ daily activities. Drawing on the Technology Acceptance Model (TAM), this study examined how and why individuals adopt these platforms and specifically addressed how adoption patterns and constructs differ between short-form and long-form video contexts.MethodsA self-reported online survey was conducted to collect data from 351 university-aged TikTok and YouTube users. Confirmatory factor analysis and structural equation modeling were employed to evaluate the model and test hypothesized path relationships.ResultsOur participants’ use of TikTok and YouTube was directly predicted by perceived ease of use and usefulness, with platform attitude serving as a key structural antecedent to actual usage. Comparative SEM analysis demonstrated that the TAM possessed significantly higher predictive validity for actual usage on TikTok than on YouTube. In addition, t-tests showed significant mean-level differences in user evaluations such that TikTok scored higher in perceived ease of use and actual usage, while YouTube scored higher in perceived usefulness and overall attitude.DiscussionThis study extends TAM literature to modern video-sharing platforms, providing significant insights into how young adults accept each platform and whether there are significant differences between TikTok and YouTube use.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1966725</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1966725</link>
        <title><![CDATA[Cryptographically verifiable authorization for autonomous AI agents: a falsifiable hypothesis and proof of concept]]></title>
        <pubdate>2026-09-24T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Mar Llambí-Morillas</author><author>Daniel Fernández-Fernández</author>
        <description><![CDATA[Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority but do not inherently provide cryptographic evidence that a concrete request issued by a specific agent satisfies the applicable policy in a specific execution context. This study hypothesizes that agent authorization can be formalized as a cryptographically verifiable relation, denoted RCVA, that jointly binds an agent principal, a concrete authorization request, an execution context, and the satisfaction of an applicable policy, while selectively preserving the confidentiality of private authorization attributes. We introduce a preliminary formal abstraction for Cryptographically Verifiable Agent Authorization (CVA), define a compact set of candidate security properties including authorization soundness, principal binding, request binding, policy binding, and replay resistance, and provide an executable zero-knowledge proof of concept that instantiates selected elements of the model over a Groth16 zk-SNARK construction. We further identify and formalize the structural separation among identity binding, authorization-request binding, and runtime execution binding as a central open problem in the design of secure agentic systems, a distinction to our knowledge, has not been formalized within a cryptographically verifiable authorization relation by current agentic security frameworks, and present a falsifiable research agenda for its resolution.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947835</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1947835</link>
        <title><![CDATA[Designing a human-in-the-loop AI iterative process for research writing: a framework for preserving productive struggle]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Sarah A. Chauncey</author>
        <description><![CDATA[This Hypothesis and Theory paper addresses a design problem at the center of AI-assisted research writing: which cognitive demands should AI reduce, and which must remain the writer’s own work. Generative AI can absorb the effort of drafting, organizing, and synthesizing prose, but that same effort is where understanding takes shape. Recent empirical work documents reduced neural engagement, weaker recall of one’s own writing, and a diminished sense of authorship among writers who rely heavily on AI assistance. The paper grounds this problem in cognitive load theory, which distinguishes intrinsic load (the element interactivity a task inherently requires) from extraneous load (demands that do not contribute to learning), and which treats germane processes as the working memory resources a writer allocates to intrinsic load. It draws on productive struggle and generative processing to explain why germane processing cannot be offloaded: when a tool performs the work, the processing does not transfer to the writer; it does not occur. Two research questions are posed. First, which cognitive demands in research writing constitute extraneous load that AI could reduce, and which require germane processing that must remain the writer’s own. Second, how can human-in-the-loop AI collaboration be designed across the phases of research writing so that this distinction is operationalized through human judgment? The contribution is a nine-phase conceptual framework mapped to the sections of a research paper, from curiosity and inquiry through references and submission. Each phase specifies the dominant cognitive demand, the AI role (ranging from minimal involvement to full collaboration), and design guidelines for tools and interactions. The framework addresses research writing from secondary school (grades 9–12) through postdoctoral and professional research, across disciplines. It treats phase-aware AI use as responsive pedagogy applied to research writing: instruction and tool design that respond to the cognitive demands of the task rather than to the tool’s capabilities. The paper draws implications for the design of responsive digital education, including tool selection, assessment, equity, and support for writers with cognitive differences. The framework is intended to preserve the cognitive work that makes research meaningful.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1893669</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1893669</link>
        <title><![CDATA[Predicting female and male participants' contact-exchange decisions from time-segmented multimodal behavioral features in speed dating]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Asahi Ogushi</author><author>Naoki Azuma</author><author>Ryo Ishii</author><author>Akihiro Miyata</author>
        <description><![CDATA[Observable behaviors associated with favorable impressions and mutual interest are difficult to identify in face-to-face romantic communication. This study examined whether time-segmented multimodal behavior could predict contact-exchange decisions and analyzed the behavioral features used by the fitted models. Using the Multi-Modal Speed Dating corpus of Japanese participants, we divided each 10-min conversation into ten 1-min intervals and extracted visual, audio, linguistic, and interpersonal-interaction features. We trained separate Random Forest binary classifiers for contact-exchange decisions made by female and male participants, used SHAP for feature selection and feature-importance analysis, and evaluated the models using fully nested participant-level cross-validation. The all-feature configuration yielded the highest observed weighted F1 for female participants' decisions (0.6248), whereas the visual-feature configuration yielded the highest for male participants' decisions (0.5379). Across the eight evaluated configurations (four feature sets for each decision group), Random Forest weighted F1 scores were numerically higher than the mean class-proportion random baseline, but every 95% interval for the difference included zero. In the all-feature model for female participants' decisions and the visual-feature model for male participants' decisions, features related to female participants' AU04 (brow lowering) ranked among the highest aggregate mean absolute SHAP features. Interval-level mean absolute SHAP share was highest at 6–7 min for female participants' decisions and at 7–8 min for male participants' decisions. These findings suggest that facial behavior and its temporal distribution may be informative for predicting contact-exchange decisions. However, because feature rankings varied across evaluations with different held-out participants, independent data are needed to determine whether the observed patterns remain stable across samples.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1925314</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1925314</link>
        <title><![CDATA[Experimental evaluation of compression algorithms for memory-constrained embedded systems]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tomáš Bača</author><author>Lukáš Formanek</author><author>Peter Ševčík</author><author>Ondrej Karpiš</author>
        <description><![CDATA[IntroductionEmbedded systems are constrained by limited flash memory, RAM (random access memory), and processing power, making efficient local data storage and transmission challenging. This study evaluates the suitability of several lossless compression algorithms for resource-constrained embedded platforms and assesses how data representation influences compression performance.MethodsLZW, Heatshrink, LZ4, FastLZ, and Miniz were evaluated directly on an STM32U575ZI microcontroller (Arm Cortex-M33, 160 MHz). A 104,067-byte sensor dataset comprising repetitive time-series measurements of soil and ambient temperature, humidity, battery voltage, timestamps, and identifiers was used as the test workload. The dataset was stored in three formats: CSV (comma-separated values) (104,067 bytes), binary (33,576 bytes), and bit-packed binary (26,581 bytes). Compression ratio, execution time, and total application RAM consumption, including input and output buffers, were measured.ResultsMiniz achieved the highest compression efficiency, reducing the 33,576-byte binary dataset to 11,352 bytes (33.81% of the original size). The fastest compression was obtained with LZ4 configured with acceleration = 16, which compressed the bit-packed representation in 9.437 ms and produced a 25,692-byte output. Among configurations selected for minimum compressed output, FastLZ achieved the shortest execution time at 31.653 ms. When total RAM consumption was evaluated consistently, including all required buffers, LZ4 operating on the bit-packed representation required the least memory among the minimum-output configurations, consuming 58,496 bytes.DiscussionThe results demonstrate significant trade-offs between compression ratio, execution speed, and memory requirements in embedded environments. Data representation substantially influences compression effectiveness, with binary and bit-packed formats generally improving efficiency over CSV. The findings provide practical guidance for selecting compression algorithms in resource-constrained embedded systems, where minimizing storage, execution time, or memory usage may be prioritized depending on application requirements.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1951219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1951219</link>
        <title><![CDATA[Correction: Establishing reference points for artificial social agent evaluation: the ASAQ representative set 2025]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Siska Fitrianie</author><author>Amal Abdulrahman</author><author>Merijn Bruijnes</author><author>Willem-Paul Brinkman</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937190</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937190</link>
        <title><![CDATA[Replications, Revisions, and Reanalyses: Managing Empirical Evidence in Software Engineering]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Julian Frattini</author><author>Jannik Fischbach</author><author>Davide Fucci</author><author>Michael Unterkalmsteiner</author><author>Daniel Mendez</author><author>Robert Feldt</author><author>Richard Torkar</author>
        <description><![CDATA[BackgroundOne aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. In software engineering (SE) research, variance theories quantify—among others—the impact of tools, techniques, and other treatments on software development outcomes like productivity, cost-efficiency, and defect-detection rates. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study.GapHowever, research synthesis in SE is rare and—if done—mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations.GoalWe aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution.MethodWe apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence.ResultThe framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research.ConclusionThe framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1844484</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1844484</link>
        <title><![CDATA[Enhanced detection of regular graphical passwords in Passpoints through convex hull area analysis]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joaquín Alberto Herrera-Macías</author><author>Carlos Miguel Legón-Pérez</author><author>Lisset Suárez-Plasencia</author><author>Omar Rojas</author><author>Guillermo Sosa-Gómez</author>
        <description><![CDATA[IntroductionPasspoints is a graphical authentication system presented as a viable alternative to traditional methods, owing to its security, usability, and low implementation cost. It requires the user to select five ordered points in an image, the configuration fixed by the Passpoints scheme. If the points selected by a user follow a regular pattern, i.e., they repel each other, the password is not secure. The existing literature reports that statistical tests can detect such a pattern at the registration stage of this system; however, their effectiveness is limited.MethodsIn this document, a new, highly effective test is proposed to detect a regular pattern in Passpoints passwords, based on the area of the convex hull formed by the five points. The reference (null) distribution of this area under complete spatial randomness is estimated by simulation and approximated by a Weibull law, whose adequacy is assessed with the Anderson—Darling, Kolmogorov—Smirnov and Chi-square goodness-of-fit tests using a parametric bootstrap, since the parameters are estimated from the same sample. Classification of an individual password is then carried out by a one-sided tail test of that single observation against the reference distribution—a discordancy test, not a goodness-of-fit test—and the Type I and Type II errors are estimated by simulation. The proposed test is compared, in effectiveness and efficiency, against the two strongest baselines in the literature: the mean-distance test and the test based on the perimeter of Delaunay triangles.ResultsOn simulated password databases, the proposed test attains the highest detection rate reported to date for this pattern, reaching 87% at α = 0.05 under the lower of the two simulated regularity levels and exceeding the detection rate of previous tests by more than 50% under matched conditions, while also attaining the lowest execution time among the tests compared.DiscussionThese figures characterise performance on passwords generated by a spatial inhibition process; detection performance on passwords chosen by real users has not yet been measured and is the subject of ongoing work.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834304</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834304</link>
        <title><![CDATA[Usability evaluation of virtual reality-based cognitive intervention for older people: a systematic review]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Tek-Yong Lim</author><author>Zhiqiang Luo</author>
        <description><![CDATA[Usability is one important criterion in evaluating the virtual reality-based cognitive intervention for older people. However, only a small number of research studies have evaluated the usability of VR-based cognitive interventions, as identified in medical and scientific databases. The present systematic review fills a gap in the literature by focusing on usability evaluation from the perspective of computing professionals since all reviewed studies were searched from two major digital computing databases. This review emphasizes the four main components of usability evaluation: participants (who?), virtual-reality-based cognitive intervention hardware (what?), context of use (which?), and evaluation method (how?). The findings reveal that most participants in the evaluations were healthy older people, head-mounted displays and controllers were frequently used as virtual reality hardware, most virtual reality content supported a task-oriented approach for individual use, and the observation method was most commonly used in usability evaluation. Most usability evaluations included both quantitative and qualitative measures. The limitations of existing usability evaluations are identified. This review encourages computing professionals to increase awareness of usability in designing and developing virtual reality-based cognitive interventions, especially for older people.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1960780</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1960780</link>
        <title><![CDATA[Hybrid indoor localization using Wi-Fi–magnetic LSTM fingerprinting and vision-based distance fusion with EKF and particle filtering]]></title>
        <pubdate>2026-09-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bawar Abid Abdalla</author><author>Halgurd S. Maghdid</author><author>Azhin T. Sabir</author>
        <description><![CDATA[Accurate indoor localization remains challenging because Wi-Fi signals are affected by multipath propagation, signal fluctuation, and environmental changes, while vision-based methods may suffer from occlusion, perspective distortion, and lighting variation. This paper proposes a hybrid indoor localization framework that combines Wi-Fi Received Signal Strength Indicator (RSSI), magnetometer fingerprints, vision-based distance estimation, and probabilistic fusion to tackle these aforementioned challenges. The framework was evaluated in a third-floor indoor corridor environment at the Faculty of Engineering, Koya University, Iraq, using a 1 m grid consisting of 344 indoor reference positions. First, a Long Short-Term (LSTM)-based fingerprinting model estimates the initial indoor X, Y coordinate using Wi-Fi RSSI and magnetometer readings. Second, a vision-based model estimates the camera-person distance from Closed-Circuit-Television (CCTV) images using pose detection, depth-related features, geometric mapping, and residual correction. Finally, the LSTM coordinate and vision-derived distance are fused using Extended Kalman Filter (EKF) and particle-filter-based methods (PF), separately. The LSTM model achieved an exact-coordinate accuracy of 80.67% and an Root Mean Square Error (RMSE) of 1.5559. The vision model achieved an average distance error of 0.159 m. The fusion improved localization accuracy, reducing the error to 0.70 m using EKF and 0.40 m using the particle-filter-based method. The results show that vision-derived distance constraints can significantly improve Wi-Fi–magnetic fingerprint localization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883327</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883327</link>
        <title><![CDATA[Forecasting sports outcomes through machine learning]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Andres Gregori</author><author>Ivan Reyes-Pedroza</author><author>Ari Yair Barrera-Animas</author><author>Julieta Noguez</author><author>David Escobar-Castillejos</author>
        <description><![CDATA[IntroductionMachine learning has increasingly been applied to forecast sports outcomes, assess player performance, estimate injury risk, and model in-game events, yet the reported evidence remains fragmented across sports and validation practices.MethodsTo address this fragmentation, this systematic review followed PRISMA 2020 reporting principles and analyzed 118 English-language journal articles published between 2020 and 2025 in Scopus and Web of Science, coding each study by prediction domain, sport discipline, algorithm family, dataset type, evaluation metric, and validation approach.ResultsMatch outcome prediction and player performance assessment accounted for most studies, whereas injury prediction and in-game event forecasting were less frequent and drew on smaller per-domain samples. Random Forest, Gradient Boosting, XGBoost, Support Vector Machines, Neural Networks, and Logistic Regression were the most common methods: ensemble models predominated in structured tabular datasets, whereas deep learning was concentrated in video, tracking, temporal, and multimodal contexts. Reported performance was difficult to compare directly because studies used heterogeneous metrics, partitions, datasets, and validation schemes.DiscussionThese patterns indicate that data access, task design, and validation quality matter as much as algorithm choice; future work should therefore prioritize open benchmarks, external validation, and causal modeling, while privacy-aware data governance, uncertainty estimation, and interpretability remain underdeveloped priorities for the field.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940743</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1940743</link>
        <title><![CDATA[Beyond activity counts: object-mediated participation in project-based learning is associated with outcomes at different levels of analysis]]></title>
        <pubdate>2026-09-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anton I. Kutuzov</author><author>Anna V. Bogdanova</author>
        <description><![CDATA[Learning analytics of collaborative project-based learning still relies largely on activity counts and final products, which register the fact of digital activity but not the structure of collaborative participation. This study proposes an object-mediated, multi-platform approach that reconstructs joint work from digital traces as a network of ties between students and shared project artifacts, and relates the structure of digital participation to educational outcomes. Drawing on engineering project-based learning at the Moscow Institute of Electronics and Mathematics, HSE University (14,289 digital events from GitLab, Wekan and Trello, 614 student–project pairs in 251 projects), we computed volume, configuration, network-position and temporal measures of participation and estimated their associations with first-attempt project grades using rank correlations with cluster-bootstrap confidence intervals, effect sizes, multilevel decomposition and hierarchical regression. Only students with at least one observed event could be included: 838 of 1,452 eligible pairs (57.7%) left no observable trace, and observed and non-observed pairs did not differ in grade. Associations are small throughout. Activity volume is weakly associated with outcomes (ρ = +0.22), as is a student’s relative embeddedness within the team once within-project ranks are placed on a common scale (ρ = +0.16). The clearest pattern is categorical rather than graded: students who left events alongside observable teammates yet shared no artifact with any of them scored markedly lower (δ = −0.34), and this indicator is stable across pooled, mixed-effects and ordinal specifications, whereas gradations among connected students add little. A less closed structural position is associated with higher grades independently of activity volume (Burt constraint ρ = −0.190), and continued expansion of a student’s circle of shared objects is associated with higher outcomes within teams. Temporal organisation contributed no incremental variance between projects but did so within them, and its direction depends on the anchor used to close the project window. The approach offers a diagnostic vocabulary for collaboration and prompts for formative enquiry rather than an instrument for grading.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1884397</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1884397</link>
        <title><![CDATA[LSTM GRFF-Net: hybrid long short-term memory-gated recurrent fractional fusion network for multimodal biometrics recognition]]></title>
        <pubdate>2026-09-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>N. Mekala</author><author>N. Mohanasundaram</author><author>R. Santhosh</author>
        <description><![CDATA[IntroductionRecently, multimodal biometric systems have been employed frequently in various fields, however, the existing methods utilized are more complex and endure accuracy issues. Therefore, a new model known as a Long Short-Term Memory-Gated Recurrent Fractional Fusion Network (LSTM-GRFF-Net) is developed for multimodal biometric recognition.MethodsFirst, the images of palmprint, finger vein, finger knuckle, iris, and fingerprints are pre-processed using the Medav filter. Next, the repeated line tracking model is utilized for tracking the finger veins, and further, the complete local binary pattern and local Gabor XOR pattern feature is mined from preprocessed finger knuckle and palmprint images. Simultaneously, iris template extraction and minutiae extraction are carried out using the pre-processed iris and fingerprint images. Then, the features are fused using the hiking optimization algorithm, and multimodal biometric recognition is performed through the proposed LSTM-GRFF-Net. The proposed architecture integrates LSTM and gated recurrent unit learning branches through a fractional calculus-based fusion layer that preserves long-range multimodal feature dependencies and enhances discriminative biometric representation.ResultsFurthermore, the LSTM-GRFF-Net measured a maximum accuracy, and true acceptance rate of 92.899 and 89.876%, where a minimum and false rejection rate and false acceptance rate value of 5.988 and 9.877% are attained. Discussion: The proposed architecture integrates LSTM and gated recurrent unit learning branches through a fractional calculus-based fusion layer that preserves long-range multimodal feature dependencies and enhances discriminative biometric representation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1878274</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1878274</link>
        <title><![CDATA[Knowledge- and memory-based interactive task learning for human robot collaboration]]></title>
        <pubdate>2026-09-16T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Michaela Kümpel</author><author>Manuel Scheibl</author><author>Omar Eldardeer</author><author>Jan-Philipp Töberg</author><author>Jeroen Schäfer</author><author>Alexis Maldonado</author><author>Alessandra Sciutti</author><author>Britta Wrede</author><author>Michael Beetz</author>
        <description><![CDATA[This work proposes a novel framework for interactive task learning that combines essential task knowledge with experiential tuning, a combination we need for cognition-inspired learning for robots. Interactive task learning is an approach inspired by research on cognitive development that promotes story-based memory structures for learning the essence of a task, which are extended and optimized over time by experiences. The proposed framework combines two recent approaches: (1) deploying actionable knowledge graphs (AKGs) as a knowledge base to help robots understand and generalize tasks and (2) integrating Narrative-Enabled Episodic Memories (NEEMs) as semantic and explainable memory structures for experiences. We integrate AKGs into the NEEM structure to benefit from the actionable function description as part of a structure that improves by ongoing and consecutive interactions. If robots grasp the essence of a task, they can transfer knowledge to similar ones. For example, they can use the knowledge of “cutting bread” to “slicing a cucumber,” distinguishing between similar tasks like cutting and slicing, but also reasoning about the similarity of needed tools and motions. Our framework links the existing concepts AKGs and NEEMs in a scenario of task demonstrations for robots to reason about semantic task and object information. By inferring task parameters like cut positions, pour angles, and stop conditions directly from AKGs, robots can achieve task generalization across diverse actions. Our example study setup for the proposed model illustrates this for household tasks, focusing on fruit-cutting and pouring. This is a hypothesis paper: we present the framework and two planned study designs, and we do not report robot experiments or task-success measurements. An example NEEM can be accessed and queried on the resource website1.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813208</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813208</link>
        <title><![CDATA[A model-driven framework for automated UI prototyping with the two-hemisphere model]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kristaps Babris</author><author>Oksana Nikiforova</author><author>Leszek Maciaszek</author>
        <description><![CDATA[IntroductionAutomated generation of executable user interfaces from domain models remains a challenging problem in model-driven software engineering, particularly when both application behavior and domain structure must be preserved in the resulting interface. Existing model-based approaches often remain at the level of abstract interface specifications, whereas AI-assisted generation provides flexible code synthesis but offers limited deterministic traceability to formal domain models. This study proposes a model-driven framework for automatically generating executable web user interface prototypes from the two-hemisphere model, which integrates process and concept representations of a problem domain.MethodsThe proposed transformation pipeline maps processes to application states and navigation structures, concepts and their attributes to data-oriented interface elements, and process semantics to reusable user interface patterns selected through a deterministic scoring mechanism. A proof-of-concept implementation generates React.js/Material-UI prototypes incorporating routing, state management, CRUD structures, and automatically generated demonstration data. The framework was evaluated using an e-commerce case study through three complementary methods: static source-code analysis, ISO/IEC/IEEE 29119-based functional acceptance testing, and a pilot expert heuristic evaluation based on Nielsen's usability heuristics.ResultsThe generated prototype passed all 25 functional acceptance-test scenarios, required no post-generation source-code modification, and was generated in approximately 12 seconds. Static analysis identified no JSLint or ESLint issues and only two minor JSHint warnings. Three independent usability evaluators produced 22 individual observations, which were consolidated into nine unique usability problems: three cosmetic, five minor, one major, and no critical violations. The major issue concerned the absence of a clearly visible order summary in the generated shopping-cart interface.DiscussionThe results provide preliminary evidence that the two-hemisphere model can serve as a viable source representation for generating executable user interface prototypes while preserving traceability between behavioral and structural domain information and the resulting implementation. The heuristic findings also identify concrete opportunities for improving transformation rules and the user interface pattern library. However, broader generalisability, comparative efficiency, accessibility, and end-user usability remain to be established through multi-case and user-centred evaluation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834280</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1834280</link>
        <title><![CDATA[Further elaboration on acoustic-assisted indoor pedestrian dead reckoning—The extended implementation details and new evaluations]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yang Wang</author><author>Takuya Maekawa</author>
        <description><![CDATA[Indoor pedestrian dead reckoning (PDR) using smartphone inertial sensors suffers from accumulated errors, so researchers use various indoor landmarks for correction. This study proposes acoustic sensing—using a smartphone's speaker and microphone to detect doors and walls—as a new ubiquitous landmark requiring no extra hardware or environment-specific training data. We present a neural-network-based acoustic event detector for door-passing and wall-proximity events with wall-distance estimation, and a particle-filter trajectory algorithm that uses these landmarks and map matching to correct inertial drift for offline trajectory reconstruction. Compared to the conference version, this paper adds implementation details, further experiments, and a thorough discussion of the method's limitations. Consistent with the conference version, results show that under relatively ideal conditions and a strict grasping posture, the method substantially outperforms the NeuralPDR baseline. However, the expanded evaluation shows that landmark recognition degrades sharply when grasping posture changes or obstacles/noise are present, that trajectory error grows exponentially at low landmark recall, and that the method's real-time processing, power, and memory demands remain impractical.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1900546</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1900546</link>
        <title><![CDATA[Agent-access receipts for artificial intelligence-mediated content and application programming interface access: a reproducible synthetic security experiment]]></title>
        <pubdate>2026-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anton Sokolov</author>
        <description><![CDATA[Artificial intelligence (AI)-mediated web and application programming interface (API) access is often treated as a bot-management problem—identify an automated caller and then allow, block, delay, challenge, or meter the request. That framing is useful but incomplete. A disputed access decision can depend on the caller’s authenticated identity, the represented principal, delegated authority, declared purpose, and resource or API surface. It can also depend on the current policy snapshot, response sensitivity, payment or licensing state, and the survival of validation evidence after the request has completed. This article presents a reproducible synthetic experiment using an agent-access receipt—a compact decision record generated at a publisher or API edge and preserved for later inspection when corresponding validation material remains available. The study implements a deterministic Python laboratory with a synthetic policy and synthetic trust registry, evaluating 15 scenario cases, 15 mutation/control cases, and 3 post-issue receipt-integrity controls. It also emits generated receipts in JavaScript Object Notation (JSON), a JSON Schema, and result tables in comma-separated values (CSV) and JSON formats. In the deposited v0.1.6 package, all 15 scenario cases, all 15 mutation cases, and all 3 integrity controls match expected outcomes, and all 30 generated receipts pass the required-field/schema-shape guard. The artifact is not a production security protocol; it does not claim legal compliance, deployment readiness, or endorsement by any third party. The article contributes a reproducible controlled experiment showing why AI-mediated access-control evidence should preserve bindings among identity, delegation, declared purpose, resource/API inventory, economic authorization, policy freshness, sensitivity, and validation survivability. The deposited artifact provides data and code for rerunning and inspecting the experiment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1904025</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1904025</link>
        <title><![CDATA[The Mercer Advisors data breach through the essential eight: lessons and analytical limits]]></title>
        <pubdate>2026-09-10T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Indra Abeysekera</author>
        <description><![CDATA[Cybersecurity breaches orchestrated by malicious attackers can have painful consequences for victim organizations. Such events show how breaches can occur and what factors lead to them. This study offers a point of reflection from theoretical and security-posture perspectives, focusing on the Mercer Advisors Inc. data breach. While acknowledging the limitations of public records, the study contributes to the literature by examining theoretical and security-posture aspects, such as the lack of multi-factor authentication at all required levels and the possible failure to implement a robust defense-in-depth strategy, both of which can result in adverse consequences for organizations.]]></description>
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