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Volume 17
Issue 8
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Information, Volume 17, Issue 8 (August 2026) – 96 articles

Cover Story (view full-size image): Despite decades of nationwide safety improvements, rail-grade crossing incidents have persisted rather than declined. This study introduces a Persistence Index that identifies counties where incident occurrence remains resistant to further improvement after the post-2010 stabilization of national trends. By integrating temporal regime analysis, spatial statistics, and explainable machine learning, the research reveals geographic clusters of persistent incidents and identifies the operational characteristics associated with these environments. The findings provide transportation agencies with a framework for moving beyond traditional hotspot analysis toward prioritizing locations where long-term safety challenges remain concentrated, supporting more targeted and effective investments in railroad safety. View this paper
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21 pages, 4550 KB  
Article
Investigating Privacy-Preserving Federated Learning for Telecom Customer Churn Prediction Using Differential Privacy
by Alisha Sikri, Shalini Gambhir, Roshan Jameel, Sheikh Mohammad Idrees and Mariusz Nowostawski
Information 2026, 17(8), 811; https://doi.org/10.3390/info17080811 - 21 Aug 2026
Viewed by 271
Abstract
Predicting customer churn in the telecom sector is critical for retaining subscribers, maintaining brand reputation, and staying ahead of competitors. Losing customers not only reduces revenue but can also weaken long-term market position in a highly competitive industry. While machine learning has been [...] Read more.
Predicting customer churn in the telecom sector is critical for retaining subscribers, maintaining brand reputation, and staying ahead of competitors. Losing customers not only reduces revenue but can also weaken long-term market position in a highly competitive industry. While machine learning has been widely used to address this challenge, most traditional approaches depend on centralizing customer data. This raises major concerns about user privacy, data ownership, and compliance with strict regulations such as GDPR. These challenges make it difficult for businesses to fully utilize customer data while safeguarding sensitive information. In this paper, we investigate a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP). Rather than collecting all customer data in a single repository, the investigated framework enables multiple clients to collaboratively train a deep neural network while maintaining data locality during the federated training process. To further enhance privacy protection, we employ Differentially Private Stochastic Gradient Descent (DP-SGD) and add controlled noise to model updates, reducing the possibility of inferring individual data contributions. This work systematically evaluates how different privacy levels, expressed through ε and δ, influence model performance under simulated non-IID client distributions. The experiments analyze the privacy–utility trade-off using multiple evaluation metrics and compare the results with centralized and non-private federated-learning approaches. The findings show that the investigated framework maintains competitive predictive performance across a range of privacy budgets while demonstrating a clear privacy–utility trade-off. Very strict privacy budgets result in substantial performance degradation, particularly for smaller and more imbalanced datasets, whereas moderate privacy budgets maintain competitive predictive performance with limited degradation. This study highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential. Full article
(This article belongs to the Section Information Security and Privacy)
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28 pages, 3240 KB  
Article
Interpretable Smart Meter Anomaly Detection Based on Bayesian-Optimized XGBoost and SHAP
by Bolin Zhang, Chao Ma, Xiang Li, Ke Yang, Haopeng Shi, Hongjing Hao and Xiaolin Gui
Information 2026, 17(8), 810; https://doi.org/10.3390/info17080810 - 21 Aug 2026
Viewed by 232
Abstract
Smart meter anomaly detection is critical for ensuring the security and stability of smart grids. However, existing detection methods face the following limitations: insufficient feature extraction, severe class imbalance, inefficient manual hyperparameter tuning, and poor model interpretability. To overcome these limitations, we propose [...] Read more.
Smart meter anomaly detection is critical for ensuring the security and stability of smart grids. However, existing detection methods face the following limitations: insufficient feature extraction, severe class imbalance, inefficient manual hyperparameter tuning, and poor model interpretability. To overcome these limitations, we propose an accurate and interpretable anomaly detection method based on Bayesian-optimized XGBoost and SHAP. Our method integrates multi-dimensional feature extraction to enrich feature information, Borderline-SMOTE to mitigate class imbalance, Bayesian optimization to tune XGBoost hyperparameters, and SHAP to quantify feature contributions and provide model interpretability. Experimental results on the public MAD dataset demonstrate that our method consistently outperforms both classical machine learning models, including decision tree, Random Forest, XGBoost, and LightGBM, as well as representative deep learning models such as CNN, TCN, LSTM, and CNN-LSTM in binary and multi-class classification tasks, achieving superior accuracy, precision, recall, and F1 scores. SHAP analysis further reveals that three-phase unbalance features are the dominant indicators of abnormal samples, a finding highly consistent with the physical mechanisms of power systems. Our method achieves both competitive detection performance and transparent decision-making, providing an actionable solution for smart meter anomaly detection in practical engineering applications. Full article
(This article belongs to the Special Issue Innovative AI Solutions for Cybersecurity in Critical Infrastructures)
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33 pages, 2996 KB  
Review
Beyond the Black Box—A New Vector for Explainable AI Through Comparative Analysis of Logical Systems
by Said Gulyamov, Saidakhror Saidakhmedovich Gulyamov, Andrey Rodionov, Islambek Rustambekov and Munavvarkhon Mukhitdinova
Information 2026, 17(8), 809; https://doi.org/10.3390/info17080809 - 21 Aug 2026
Viewed by 598
Abstract
Modern AI often works as a “black box”: it gives an answer, but cannot show why. In high-stakes fields like law, medicine, and government—and under emerging rules such as the EU AI Act—that is a serious problem. Today’s most popular explainability tools, such [...] Read more.
Modern AI often works as a “black box”: it gives an answer, but cannot show why. In high-stakes fields like law, medicine, and government—and under emerging rules such as the EU AI Act—that is a serious problem. Today’s most popular explainability tools, such as SHAP and LIME, only approximate a model’s reasoning after the fact, and their explanations can be unstable. This review explores a different, often overlooked path: logical systems. We first explain in plain terms what they are and where they come from, then compare the main families—propositional, deontic, and first-order logic paired with modern solvers—by what each can express and guarantee. Our main contribution is a comparative taxonomy organized by explanatory guarantees, which reveals that no existing class simultaneously offers natural-language input, formal verifiability, and reproducibility. We then examine neuro-symbolic systems, illustrated by a representative engine (Causal Logic Engine, CLE), where a language model reads the text but a transparent logical layer makes the decision, checked by a human; the engine is described end to end, down to a worked example traced from raw text to the final decision. The key idea: instead of opening the black box, we move the decision outside it—so the reason behind every answer becomes clear and reproducible. Full article
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence, 2nd Edition)
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20 pages, 1191 KB  
Article
ChoreDiffusion: Beat-Aware Diffusion for Music-to-Dance Generation
by Yufei Gao, Qian Wu, Shuliang Zhu, Keren He, Wei Weng and Jinjia Zhou
Information 2026, 17(8), 808; https://doi.org/10.3390/info17080808 - 21 Aug 2026
Viewed by 290
Abstract
Music-to-dance generation requires precisely aligning movement dynamics with musical rhythm, yet existing methods rely on shallow conditioning or auxiliary beat-alignment objectives that fail to establish stable beat–motion correspondences. We present ChoreDiffusion, a diffusion-based framework that integrates explicit beat guidance directly into the denoising [...] Read more.
Music-to-dance generation requires precisely aligning movement dynamics with musical rhythm, yet existing methods rely on shallow conditioning or auxiliary beat-alignment objectives that fail to establish stable beat–motion correspondences. We present ChoreDiffusion, a diffusion-based framework that integrates explicit beat guidance directly into the denoising process. Central to our approach is a beat-enhanced cross-modal attention mechanism that injects beat-salience cues at every refinement step, promoting fine-grained synchronization beyond the reach of conventional conditioning pipelines. To support multiple dance styles within a unified model, we incorporate lightweight low-rank adaptation (LoRA) modules that encode style-specific motion signatures with only a small set of additional parameters per style, and a three-stage progressive curriculum stabilizes the joint learning of rhythmic alignment and stylistic expressivity. Experiments on two public multi-style dance benchmarks (AIST++ and FineDance) show that ChoreDiffusion achieves the lowest FID values among the compared generation methods on both benchmarks, while maintaining competitive rhythm alignment and multi-style controllability. These results indicate that embedding beat-aware guidance during generation, rather than applying it afterwards, is an effective route toward human-like musicality in music-driven choreography. Full article
(This article belongs to the Section Artificial Intelligence)
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35 pages, 1823 KB  
Review
Mapping IT Reference Frameworks for Governance, Service Management, and Quality Assurance: A Scoping Review
by Alejandro Quintero Sánchez, José Ricardo Gómez Rodríguez, Luis Alberto Flores Chaires, José Guadalupe Arceo Olague, Víktor Iván Rodríguez Abdalá and Remberto Sandoval Aréchiga
Information 2026, 17(8), 807; https://doi.org/10.3390/info17080807 - 21 Aug 2026
Viewed by 516
Abstract
The increasing complexity of information technology (IT) systems requires reference frameworks that connect governance, service management, and quality assurance. This scoping review maps 109 core sources published between 1996 and June 2026 to characterize evidence on IT reference frameworks across organizational and sectoral [...] Read more.
The increasing complexity of information technology (IT) systems requires reference frameworks that connect governance, service management, and quality assurance. This scoping review maps 109 core sources published between 1996 and June 2026 to characterize evidence on IT reference frameworks across organizational and sectoral contexts; records from 2026 are treated as partial-year data and are not interpreted as a complete annual trend. The review was structured using the Population–Concept–Context (PCC) framework and reported in alignment with PRISMA-ScR guidance. Peer-reviewed studies and selected grey literature sources were charted to identify research trends, framework families, implementation patterns, integration interfaces, evaluation practices, and evidence gaps. Dominant families included ISO/TQM-oriented quality systems, COBIT and ITIL/IT service management (ITSM), enterprise architecture, sectoral quality assurance, cybersecurity, and emerging AI/data-governance approaches. The findings indicate that integration is concentrated around recurring interfaces among strategic governance, service operation, quality and assurance controls, enterprise architecture, and evidence feedback. However, the mapped evidence remains methodologically uneven: conceptual frameworks, case studies, and reviews dominate, while longitudinal validation and comparable implementation metrics are limited. Emerging AI-related work is treated as an early research direction rather than as mature evidence of effectiveness. Full article
(This article belongs to the Section Information Systems)
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36 pages, 4122 KB  
Article
AI-Mediated Continuous Assessment Infrastructure (AIM-CAI): Connecting Learning Evidence Across Contexts and Time
by Danielle S. McNamara and Mohammad Nehal Hasnine
Information 2026, 17(8), 806; https://doi.org/10.3390/info17080806 - 21 Aug 2026
Viewed by 462
Abstract
Educational assessment systems have primarily relied on episodic forms of assessment, including examinations, assignments, grades, and credentials. These approaches provide efficient and scalable summaries of achievement and yet capture only part of the developmental process through which learners build competence. Moreover, learning increasingly [...] Read more.
Educational assessment systems have primarily relied on episodic forms of assessment, including examinations, assignments, grades, and credentials. These approaches provide efficient and scalable summaries of achievement and yet capture only part of the developmental process through which learners build competence. Moreover, learning increasingly unfolds across digital platforms, workplaces, collaborative networks, and AI-mediated environments, generating rich evidence of learner development that remains fragmented across systems and contexts. Advances in artificial intelligence, learning analytics, multimodal analytics, learner modeling, and semantic interoperability make it increasingly feasible to connect, integrate, and interpret this evidence across contexts and over time. This paper introduces the AI-Mediated Continuous Assessment Infrastructure (AIM-CAI), a sociotechnical framework supporting longitudinal, probabilistic interpretation of distributed evidence of learning. Within AIM-CAI, continuous assessment refers to the ongoing accumulation and dynamic interpretation of evidence generated through learning activities. The framework integrates distributed evidence systems, evidence serialization mechanisms, AI-mediated semantic translation, probabilistic learner models, dynamic competency profiles, and federated governance architectures to support context-sensitive interpretations of learner development while maintaining human judgment, privacy, accountability, and learner agency. The authors examine implications for assessment, credentialing, lifelong learning, institutional roles, interoperability, and governance and outline a research agenda addressing key psychometric, ethical, and governance challenges, including validity, fairness, surveillance, semantic instability, and ownership of learning evidence. Full article
(This article belongs to the Section Information Applications)
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34 pages, 4855 KB  
Article
PC-PLF: Path-Conditioned Per-Layer LoRA Fusion for Open-Vocabulary ROADWork Segmentation
by Ping Wu, Zhi-Ren Pan, Bo Qiu, Jian-Ping Wu and Shao-Jiang Zheng
Information 2026, 17(8), 805; https://doi.org/10.3390/info17080805 - 20 Aug 2026
Viewed by 319
Abstract
Construction work zones are a difficult case for open-vocabulary semantic segmentation. Their layouts are temporary, safety-relevant objects that are often small and long-tailed, and generic models readily confuse them with background. We address these failures inside an LoRA adapter space rather than retraining [...] Read more.
Construction work zones are a difficult case for open-vocabulary semantic segmentation. Their layouts are temporary, safety-relevant objects that are often small and long-tailed, and generic models readily confuse them with background. We address these failures inside an LoRA adapter space rather than retraining the backbone. Using only ROADWork training data, we audit a CAT-Seg RoadWork LoRA for false-positive- and recall-dominated cases and pair them with anchor images to train a residual adapter. Path-conditioned per-layer LoRA fusion (PC-PLF) then distributes a global correction budget across adapted layers using each layer’s first-order tangent magnitude along the stored factor path. Under group-disjoint out-of-fold evaluation on ROADWork, the complete method raises the mIoU from 61.72 for the RoadWork LoRA baseline to 62.29, with a shared-budget allocation gain of 0.33 mIoU over uniform fusion. Most of the total improvement appears before per-layer allocation. Uniform residual fusion contributes 0.69 points over the baseline, confirming that failure-driven residual training supplies the larger share; PC-PLF contributes a smaller allocation effect when tested on the same trained base-residual pair. The allocation effect is reproducible across four curation rules but near zero under SAN architecture transfer and MUSES second-target-domain evaluation. Three-group and text-weighted controls do not recover the full gain. Improvements concentrate in several long-tail safety classes. Cross-architecture, cross-dataset, and calibration audits define the operating regime rather than universal advantage. Residual curation supplies the larger share of the improvement; layer-wise allocation contributes a smaller, pair-specific gain. Full article
(This article belongs to the Topic Artificial Neural Networks for Visual Learning)
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32 pages, 3698 KB  
Article
Spatial Predictive Patterns of Cause-Specific Mortality: Evidence from East Africa
by Sally Sonia Simmons, John Elvis Hagan, Jr., Imanol L. Nieto-González and Thomas Schack
Information 2026, 17(8), 804; https://doi.org/10.3390/info17080804 - 20 Aug 2026
Viewed by 275
Abstract
(1) Background: Whether spatial predictive patterns in non-communicable disease mortality persist after accounting for socio-demographic development and biomarkers remains understudied in East Africa. (2) Methods: This study used heterogeneous graph transformer (HGT) models and other techniques to model spatial patterns in cause- and [...] Read more.
(1) Background: Whether spatial predictive patterns in non-communicable disease mortality persist after accounting for socio-demographic development and biomarkers remains understudied in East Africa. (2) Methods: This study used heterogeneous graph transformer (HGT) models and other techniques to model spatial patterns in cause- and sex/age-specific mortality (hypertensive heart disease [HHD], ischaemic heart disease [IHD], stroke, and diabetes), incorporating risk factors and socio-demographic development (SDI), using data from the Global Burden of Disease (GBD) study, 1990–2023, across Burundi, Kenya, Rwanda, Tanzania, and Uganda. (3) Results: HGT achieved higher performance than OLS spatial lag benchmarks (R2 0.948–0.970 vs. 0.194–0.376). Spatial predictive patterns were disease-specific. Stroke was the only disease with consistent positive spatial structure (SDI-only: 0.645%, 95% CI [0.380, 0.907]), with spatial structure strengthening after 2015. HHD exhibited severe and stable degradation (Risk-only: −137.892%, 95% CI [−181.908, −96.380]), driven by the interaction between metabolic risk covariates and geographic adjacency. Diabetes showed consistently severe degradation (SDI + Risk: −201.941%, 95% CI [−257.349, −150.082]). IHD patterns were weak and unstable. Sex disaggregation revealed stronger stroke spatial signals, indicating latent sex-specific patterns masked by aggregation. GBD measurement uncertainty contributed less than 0.025% of result variance, with model randomness dominating. (4) Conclusions: Spatial predictive patterns in NCD mortality in East Africa are disease-specific. Stroke shows emerging cross-border spatial structure after 2015, while HHD and diabetes reflect country-specific determinants. Sex-disaggregated graph construction reveals latent spatial heterogeneity invisible to aggregate models, supporting disease-specific, sex-stratified regional health strategies. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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33 pages, 3739 KB  
Article
SEM-PDPL: Semantic Exposure Graphs for Privacy-Law-Informed Risk Assessment of Public Social-Media Data
by Heba Ismail
Information 2026, 17(8), 803; https://doi.org/10.3390/info17080803 - 20 Aug 2026
Viewed by 322
Abstract
Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work [...] Read more.
Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work operationalizes how personal-data disclosures combine structurally and how these structures can be translated into auditable governance actions. This paper proposes SEM-PDPL, a computational, privacy-law-informed risk-assessment framework for modeling public social-media exposure as semantic exposure graphs and mapping graph patterns to controls aligned with the United Arab Emirates Personal Data Protection Law (PDPL) and compatible with GDPR principles. SEM-PDPL combines governance scoping; a PDPL-informed disclosure taxonomy; hybrid extraction using rule-based methods; named-entity recognition; fine-tuned BERT; and schema-constrained large language model annotation, followed by graph construction at post, platform, corpus, and persona levels. The framework is evaluated on a synthetic multi-platform corpus of 1095 posts generated for 150 personas across 290 platform accounts. Results show that, within this controlled synthetic corpus, fine-tuned BERT provides the strongest extraction performance among six evaluated methods, achieving a macro-F1 of 0.975. Graph analysis shows that exposure density increases with aggregation, rising from 0.275 at post level to 1.000 at corpus level, and from 0.859 at platform level to 0.967 at persona level. Across all graph resolutions, quasi-identifiers emerge as the dominant weighted-degree and betweenness node, indicating that ordinary location, employer, school, and demographic cues often function as bridges connecting sensitive categories such as health and biometric data to identifying information. These findings indicate that, within this controlled corpus, privacy risk in public social-media data is not only attribute-based but also structurally graph-shaped. SEM-PDPL contributes an explainable and reproducible framework for identifying exposure hubs, sensitive bridges, and aggregation risks before applying masking, minimization, exclusion, retention, or review controls. The framework does not automate legal compliance; rather, it provides evidence-based decision support for privacy-aware social-media analytics. Full article
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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38 pages, 10872 KB  
Review
Toward Trustworthy AI for Autism Spectrum Disorder: A Systematic Review of Multimodal Systems, Knowledge Representation, and Clinical Integration
by Rita Zgheib, Alia El Naggar, Arash Kermani Kolankeh and Aseel A. Takshe
Information 2026, 17(8), 802; https://doi.org/10.3390/info17080802 - 20 Aug 2026
Viewed by 449
Abstract
Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze [...] Read more.
Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article
(This article belongs to the Special Issue Machine Learning and Simulation for Public Health)
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4 pages, 127 KB  
Editorial
Navigated Information Behaviors, Analytics, and Digital Flourishing in the Modern Social Landscape
by Yair Galily
Information 2026, 17(8), 801; https://doi.org/10.3390/info17080801 - 20 Aug 2026
Viewed by 227
Abstract
In the rapidly evolving landscape of contemporary digital communication, the dynamic interplay between individual human information behaviors and the increasingly complex analytical tools applied to social media platform data has rapidly emerged as a foundational focal point of multidisciplinary scholarship across the social [...] Read more.
In the rapidly evolving landscape of contemporary digital communication, the dynamic interplay between individual human information behaviors and the increasingly complex analytical tools applied to social media platform data has rapidly emerged as a foundational focal point of multidisciplinary scholarship across the social and computational sciences [...] Full article
(This article belongs to the Special Issue Information Behaviors: Social Media Challenges and Analytics)
70 pages, 5491 KB  
Article
QUEST: A Simulation-Based QKD Architecture with Eight-State Time-Bin Modulation and Adaptive Homodyne–Heterodyne Detection
by Vidhya Prakash Rajendran, Deepalakshmi Perumalsamy, Basker Palaniswamy, Ashok Kumar Das and Vivekananda Bhat K
Information 2026, 17(8), 800; https://doi.org/10.3390/info17080800 - 19 Aug 2026
Cited by 1 | Viewed by 348
Abstract
Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently [...] Read more.
Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently authenticate the classical channel, and it does not prevent implementation side channels. In this work, we introduce ModPhase-8 (QUEST), a proposed QKD modulation and adaptive-receiver architecture evaluated through analytical modeling and simulation. Instead of using only a few quantum signal types, our system uses eight carefully designed signal variations created by adjusting the phase between two very short light pulses. The eight phase states are organized into four phase bases, each containing two antipodal states that encode one binary raw-key value. The enlarged signal set diversifies the physical representation of the key bit and changes the state-discrimination problem faced by an eavesdropper, but it does not increase the raw-key payload beyond one bit per successfully sifted signal. On the receiving side, the system adaptively switches between two measurement techniques based on the prevailing channel conditions. This adaptive detection mechanism enhances reliability and helps maintain low error rates even when the communication channel is affected by noise. We provide an analytical security assessment under the stated collective-attack, source, channel, receiver, and trusted-device assumptions, supplemented by attack-specific analyses of intercept–resend, beam-splitting, source-side multi-photon leakage, and selected implementation-related vulnerabilities. Simulation studies were conducted to examine the physical-layer and post-processing behavior of the proposed protocol under explicitly stated channel, receiver, detector, and finite-sample values. Under the adopted simulation model, ModPhase-8 maintains low error rates in the low- and moderate-noise operating regimes and exhibits favorable receiver-level robustness across the investigated channel conditions. The reported rate values are model-based performance estimates rather than rigorously certified secret-key lower bounds. In particular, Qiskit simulation does not establish a composable security proof or an optimal bound on Eve’s information for the exact eight-state time-bin ensemble. A protocol-specific numerical security analysis incorporating the homodyne–heterodyne measurement operators, post-selection, reconciliation efficiency, finite-size effects, and Eve’s Holevo information remains necessary before definitive rate comparisons can be made. ModPhase-8 should therefore be interpreted as a practically motivated receiver and modulation framework whose security-rate performance remains subject to further protocol-specific analysis. Full article
(This article belongs to the Special Issue Cryptographic Protocols for Decentralized Security and Privacy)
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27 pages, 6369 KB  
Article
Frequency-Dependent EEG Network Reorganization Under Transcutaneous Electroacupuncture Stimulation: Clinical Insights from Graph Analysis
by Amna Sajid, Raheel Zafar, Muhammad Zafarullah, Ata Ullah, Giuseppina Pappalardo, Shumayla Yaqoob and David Mayor
Information 2026, 17(8), 799; https://doi.org/10.3390/info17080799 - 19 Aug 2026
Viewed by 225
Abstract
The effects of transcutaneous electroacupuncture stimulation (TEAS) on large-scale brain function remain insufficiently characterized. This study employed a graph-theoretical approach to analyze electroencephalogram (EEG) data from 48 healthy participants in the Pilot-6 TEAS study. Participants received sham (0 pps), 2.5 pps, 10 pps, [...] Read more.
The effects of transcutaneous electroacupuncture stimulation (TEAS) on large-scale brain function remain insufficiently characterized. This study employed a graph-theoretical approach to analyze electroencephalogram (EEG) data from 48 healthy participants in the Pilot-6 TEAS study. Participants received sham (0 pps), 2.5 pps, 10 pps, and 80 pps stimulation during baseline, stimulation, and recovery phases. Functional connectivity was assessed using coherence and the weighted phase-lag index, followed by calculation of global and nodal graph measures from thresholded weighted undirected sensor-level networks. Descriptive analysis indicated potential frequency-related differences in EEG network organization. The 2.5 pps condition exhibited the highest average degree, whereas the 80 pps condition demonstrated the highest average clustering coefficient. At 10 pps, sensor-level maps revealed a distinct frontal–central betweenness-centrality pattern. Although 48 participants provided usable EEG data for descriptive analysis, only 3 participants had complete matched graph-metric data for all four stimulation conditions, limiting repeated-measures statistical validation. After correction for multiple comparisons, no statistically significant frequency-related effects were observed, and nodal hub differences were not independently confirmed. Consequently, these patterns should be interpreted as descriptive and exploratory rather than established group-level effects. These findings indicate that graph-theoretical EEG analysis may facilitate the identification of candidate network features for future investigations of TEAS-related brain network organization. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing)
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50 pages, 10461 KB  
Article
Agile Software Development Challenges: Identification, Validation, and Prioritization Using the Analytic Hierarchy Process
by Kamran Khan Tatari, Shahid Latif, Salim Ur Rehman and Muhammad Ismail Mohmand
Information 2026, 17(8), 798; https://doi.org/10.3390/info17080798 - 19 Aug 2026
Viewed by 360
Abstract
Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to [...] Read more.
Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to their ranking and prioritization, which are critical for effective project management and decision making. This study fills this gap by combining empirical evidence from the literature and practitioners. Objectives: This study aims to identify and hierarchically prioritize the most recent challenges faced by Agile practitioners during product development. To achieve this, a Systematic Literature Review (SLR) was conducted using 115 published studies between 2010 and 2025 followed by empirical data collection from 30 Agile experts through semi-structured interviews conducted with practitioners from Agile companies and an online survey. This study applies Cumulative Voting (100-Dollar Test) and Multi-Criteria Decision Making (MCDM) techniques to rank and prioritize these challenges. Results: The SLR identifies several recurring Agile challenges; however, limited research has focused on their ranking and prioritization. The present study reveals new challenges, such as user interface complexities, lack of pre-development and pre-operational cost information, and lack of cost scalability at the module and feature levels. The current study identifies Inadequate Architecture (22%), Lack of Standardized Framework (18%), Communication and Coordination (16%), Poor Requirement Verification (13%), and Minimum Documentation (8%) as the most significant challenges. Conclusions: This study provides valuable insight for Agile practitioners and organizations, enabling more informed project planning, resource allocation, and strategic decision making. By focusing on the most critical challenges, teams can enhance software quality, streamline processes, and improve overall productivity in Agile environments. Full article
(This article belongs to the Topic Fuzzy Optimization and Decision Making)
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25 pages, 2439 KB  
Article
GAD-YOLO: A Multi-Level Feature Enhancement Network for Dense Small Traffic Object Detection in Intelligent Transportation Systems
by Yuan He, Xing Li, Junfa Zhu, Lina Zhang, Dengqi Yang and Xiaowei Li
Information 2026, 17(8), 797; https://doi.org/10.3390/info17080797 - 19 Aug 2026
Viewed by 235
Abstract
Dense small traffic object detection is essential for intelligent transportation systems but remains challenging because distant targets contain limited visual details, densely distributed objects frequently overlap, and complex road backgrounds introduce substantial interference. To address these limitations, this study proposes GAD-YOLO, a multi-level [...] Read more.
Dense small traffic object detection is essential for intelligent transportation systems but remains challenging because distant targets contain limited visual details, densely distributed objects frequently overlap, and complex road backgrounds introduce substantial interference. To address these limitations, this study proposes GAD-YOLO, a multi-level feature enhancement network based on YOLOv9. Ghost-MSConv performs lightweight multi-receptive-field feature extraction in the backbone, Mixed Local Channel Attention combines local spatial relationships with global channel dependencies during feature refinement, and DySample performs content-adaptive point sampling during feature upsampling. In the primary experiments on a six-class traffic object dataset derived from UA-DETRAC, GAD-YOLO achieved a precision of 78.9%, a recall of 76.4%, an mAP50 of 82.8%, and an mAP50:95 of 65.5%. Compared with YOLOv9c, precision, recall, mAP50, and mAP50:95 increased by 5.4, 0.5, 3.1, and 4.8 percentage points, respectively. Under the complexity statistics used in the primary experiments, GAD-YOLO contains 25.455 M parameters and requires 102.4 GFLOPs, compared with 25.442 M parameters and 103.2 GFLOPs for YOLOv9c. Additional experiments on the public VisDrone2019-DET benchmark were conducted to evaluate cross-dataset applicability, small-object performance, scene-density sensitivity, and standardized inference efficiency. On the VisDrone2019-DET test-dev set, GAD-YOLO improved mAP50 and mAP50:95 from 26.5% and 15.7% to 27.1% and 16.3%, respectively. A COCO-style analysis further showed that APS increased from 6.72% to 7.31%, while the dense-subset mAP50:95 increased from 13.95% to 14.44%. Under an RTX 4090, batch-size-one, 640×640, FP32 inference protocol, GAD-YOLO achieved a mean latency of 9.98 ms and a throughput of 100.20 FPS. These results show that GAD-YOLO improves the primary traffic object detection task and yields modest positive performance differences on an independent public benchmark under the fixed experimental setting, while maintaining real-time inference capability. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems)
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18 pages, 435 KB  
Article
Numerical Search for Extensions of Tensor-Product Mutually Unbiased Bases in Non-Prime-Power Composite Dimensions up to 100
by Jeffery Wu, Ziyuan Liu and Shengjun Wu
Information 2026, 17(8), 796; https://doi.org/10.3390/info17080796 - 19 Aug 2026
Viewed by 229
Abstract
For every prime-power dimension, a complete set of d+1 mutually unbiased bases (MUBs) is known, but in non-prime-power composite dimensions, the maximum number N(d) remains open. The tensor-product construction supplies [...] Read more.
For every prime-power dimension, a complete set of d+1 mutually unbiased bases (MUBs) is known, but in non-prime-power composite dimensions, the maximum number N(d) remains open. The tensor-product construction supplies mini(piai)+1 MUBs for d=∏ipiai, and this is the best lower bound currently known for every non-prime-power composite d≤100. We do not attempt to determine N(d); we ask whether these specific tensor-product sets admit one additional basis. We build and verify the sets for all 64 such dimensions, with pairwise overlap deviations below 10−15; run a construction-free joint search for d≤7; compare twelve optimizers; and test construction, convergence, and success thresholds. For a specified protocol A—algorithm, initialization distribution, success criterion, and stopping rule—let qA(d) denote the probability that one descent recovers a provably existing extension. Among the successful local-descent protocols tested, the inferred probabilities have comparable order, whereas the number of descents per computational budget differs much more strongly. With maxfun unbounded, exact gradients and optimization on U(d) recover guaranteed extensions at d=10,12, and 16. Raising the Riemannian CG iteration limit from 6000 to 150,000 changes none of the success counts or extension-search summaries. In d=6, the numerical failure to extend the tensor-product-basis triple reproduces a known analytic unextendibility theorem; it does not resolve the general four-MUB problem. Moreover, the actual extension target is maximally entangled. Haar-random initialization has zero probability of lying exactly on that structured submanifold, and the unrestricted search has not been validated for convergence to it, so recovery probabilities from the tensor-basis validation task cannot be transferred directly to the extension problem. The constructed bases, numerical summaries, per-restart arrays for the optimizer and threshold studies, complete per-descent arrays, and all code are publicly archived. Full article
(This article belongs to the Special Issue Advances in Quantum Information Processing: Theory, Methods and Emerging Applications)
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15 pages, 362 KB  
Article
Time-Series and Social-Media Threat Analytics over a Deployed Cyber-Threat Knowledge Graph
by Kalin Kopanov, Kristina Dineva, Ivaylo Keremidarski, Velizar Varbanov, Vitalii Toderian, Petrica Butusina and Andrei Ionut Damian
Information 2026, 17(8), 794; https://doi.org/10.3390/info17080794 - 19 Aug 2026
Viewed by 341
Abstract
Security teams decide which vulnerabilities to patch first, which alerts to trust, and whether social media warns of new threats earlier than the official feeds. We answer these questions by directly measuring EdgeGuard, a deployed cyber-threat knowledge graph that merges eleven public threat [...] Read more.
Security teams decide which vulnerabilities to patch first, which alerts to trust, and whether social media warns of new threats earlier than the official feeds. We answer these questions by directly measuring EdgeGuard, a deployed cyber-threat knowledge graph that merges eleven public threat feeds into one Neo4j database via MISP (an open threat-sharing platform) and the STIX 2.1 exchange format, recording for every entry which feed reported it and when. These records let the graph be read as a time series. Read this way, it shows that half of the vulnerabilities known to have been exploited were listed as exploited within five days of their publication (352 cases), and that a large ingestion spike in early 2026 came from a single feed rather than a real attack wave. Benchmarked against 10,000 threat-related social-media posts, the graph already held 96% of the actionable vulnerabilities the posts discussed and reported them at least as quickly, while most posts carried no actionable signal and social media led only in early warning of active exploitation. A crowd-sourced community layer additionally supplies the only intelligence tagged by industry sector. The deployed graph is thus a clean, timely, and comprehensive base, and live social ingestion a small, targeted enhancement. Full article
(This article belongs to the Special Issue Convergence of Time-Series Analytics and Social Media Intelligence)
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24 pages, 841 KB  
Article
DEGA: A Deterministic Diagnostic Evidence Governance Agent for Industrial IoT—A DUDU-BLDC Case Study
by Waldemar Bauer, Kacper Jarzyna, Agnieszka Piątek, Miłosz Ziemba and Jerzy Baranowski
Information 2026, 17(8), 795; https://doi.org/10.3390/info17080795 - 18 Aug 2026
Viewed by 246
Abstract
Industrial diagnostic systems require a governance mechanism that determines whether heterogeneous evidence is admissible, mutually consistent, and sufficient for an automatic action. This paper presents the Diagnostic Evidence Governance Agent (DEGA), a deterministic governance layer built from explicit finite-state machine states, replaceable routing [...] Read more.
Industrial diagnostic systems require a governance mechanism that determines whether heterogeneous evidence is admissible, mutually consistent, and sufficient for an automatic action. This paper presents the Diagnostic Evidence Governance Agent (DEGA), a deterministic governance layer built from explicit finite-state machine states, replaceable routing policies, an authoritative SafetyGuard, and hash-linked audit with deterministic replay. The bounded DUDU-BLDC case study retains diagnostic evidence from eight acquisitions and extends the evaluation to governance-profile sensitivity and state coverage. Across 15,360 retained real-evidence case-policy routes, no automatic recommendation was issued; 9216 routes reached Decision Check; and the mandatory-explanation rule triggered 7904 times. Under predeclared contract-satisfied simulated profiles, 2924 recommendations and 12,436 escalations were produced, with no recommended route violating the internal governance-consistency proxy. The results show that explanation availability is a binding governance condition and that recommendation-path reachability can be evaluated without weakening the remaining SafetyGuard gates; the simulation is restricted to explanation availability and admissibility metadata and does not validate feature attribution, recommendation correctness, or industrial deployment. Full article
(This article belongs to the Special Issue IoT, AI, and Blockchain: Applications, Security, and Perspectives)
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29 pages, 1779 KB  
Article
Efficient Software Vulnerability Detection Using Edge-Conditioned Graph Neural Networks on Heterogeneous Code Property Graphs
by Ahmed M. Elalfy, Gamal A. Ebrahim and Marvy Badr Monir Mansour
Information 2026, 17(8), 793; https://doi.org/10.3390/info17080793 - 18 Aug 2026
Viewed by 380
Abstract
Software vulnerabilities are a primary cause of security breaches, and their automated detection at scale has therefore become a pressing concern for both industry and academia. Most Graph Neural Network (GNN) approaches to vulnerability detection treat code graphs as homogeneous structures, and the [...] Read more.
Software vulnerabilities are a primary cause of security breaches, and their automated detection at scale has therefore become a pressing concern for both industry and academia. Most Graph Neural Network (GNN) approaches to vulnerability detection treat code graphs as homogeneous structures, and the semantic distinctions between Abstract Syntax Tree (AST) edges, Control-Flow Graph (CFG) edges, and data-flow dependency edges are consequently discarded. The main objective of this study is to determine whether explicitly conditioning message passing on edge type yields accurate yet lightweight detection. To this end, an edge-conditioned GNN named FastVulnGNN is proposed, in which the message-passing computation is conditioned on edge-type information drawn from Code Property Graphs (CPGs). FastVulnGNN operates on Joern-produced CPGs that contain 33 node types and 21 edge types, so that the full heterogeneous graph structure is preserved. A multi-scale readout mechanism that combines mean, maximum, and learned attention pooling is employed for graph-level classification, and the training configuration, which combines focal loss, label smoothing, and cosine annealing warm restarts, is individually validated by an ablation of the training objective. On the MegaVul dataset of 1904 balanced C/C++ samples, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are achieved with only 71,810 parameters. Training completes in under two minutes on a single CPU core, and no GPU resources are required. The significance of this work lies in its demonstration that a compact, edge-aware architecture can match independently reproduced results of far larger models while remaining deployable in resource-constrained settings, such as continuous-integration pipelines and developer workstations. This study is deliberately framed as a controlled and reproducible engineering and evaluation contribution rather than as an architectural advance. An edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are additionally reported to characterize the behavior and limitations of the model. Full article
(This article belongs to the Special Issue Recent Advances in Smart Contract and Blockchain Analysis)
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30 pages, 3364 KB  
Article
A Multi-Attribute Predictive Analysis Model for University Student Sentiment Public Opinion Based on Big Data
by Baoguo Chen and Yongsheng Hao
Information 2026, 17(8), 792; https://doi.org/10.3390/info17080792 - 18 Aug 2026
Viewed by 295
Abstract
With social media as the main channel for college students to express emotions, sentiment public opinion analysis in big data environments poses three core challenges to campus sentiment monitoring and psychological counseling: severe data noise interference, insufficient multi-attribute feature extraction, and the trade-off [...] Read more.
With social media as the main channel for college students to express emotions, sentiment public opinion analysis in big data environments poses three core challenges to campus sentiment monitoring and psychological counseling: severe data noise interference, insufficient multi-attribute feature extraction, and the trade-off between recognition accuracy and inference efficiency. This paper proposes a university student public opinion prediction model integrating multi-attribute decision-making and BERT–Mamba. First, an anti-interference matching filter cleans raw data by filtering out advertisements and irrelevant comments to improve data quality. Second, a multi-attribute decision object model extracts quantifiable attributes covering media sources, themes, and temporal dimensions. Third, BERT generates textual sentiment representations, and a three-stage deep feature extraction architecture with Mamba balances accuracy and efficiency. Finally, multi-attribute features and sentiment representations are fused for dynamic public opinion prediction. Validated using the ChnSentiCorp Chinese sentiment analysis benchmark dataset and university student Weibo public opinion corpus, the model achieves 97.44% average sentiment recognition accuracy. It provides technical support for universities to understand student sentiment trends and address negative public opinions, with practical value for enhancing campus public opinion monitoring and assisting mental health counseling. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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29 pages, 1434 KB  
Article
An Explainable Hypergraph Neural Network Framework for Intelligent Customer Segmentation and Purchase Behavior Prediction
by Kittipol Wisaeng and Thongchai Kaewkiriya
Information 2026, 17(8), 791; https://doi.org/10.3390/info17080791 - 17 Aug 2026
Viewed by 392
Abstract
Customer segmentation and purchase behavior prediction are fundamental tasks in intelligent e-commerce systems, enabling personalized marketing strategies and data-driven customer relationship management. However, conventional machine learning and graph neural network approaches primarily model pairwise interactions and often fail to capture higher-order relationships among [...] Read more.
Customer segmentation and purchase behavior prediction are fundamental tasks in intelligent e-commerce systems, enabling personalized marketing strategies and data-driven customer relationship management. However, conventional machine learning and graph neural network approaches primarily model pairwise interactions and often fail to capture higher-order relationships among customers, products, brands, and purchase contexts, limiting predictive performance and model interpretability. To address these challenges, this study proposes an Explainable Hypergraph Neural Network (EHGNN) framework that integrates higher-order hypergraph representation learning with post hoc explainability using SHAP. The proposed framework constructs a heterogeneous hypergraph from customer transaction data, learns informative customer embeddings via hypergraph convolution, segments customers via clustering, and predicts purchase behavior using an embedding fusion network. Comprehensive experiments were conducted to compare the proposed framework with conventional clustering algorithms, deep clustering methods, graph neural networks, and hypergraph neural networks. Experimental results demonstrate that the proposed EHGNN consistently achieved superior performance, obtaining a Silhouette Coefficient of 0.824, Davies–Bouldin Index of 0.336, and Calinski–Harabasz Index of 2815 for customer segmentation. For purchase behavior prediction, the proposed framework achieved an Accuracy of 97.30%, Precision of 97.00%, Recall of 96.80%, F1-score of 96.90%, Area Under the Receiver Operating Characteristic Curve (AUC) of 99.20%, and a Matthews Correlation Coefficient (MCC) of 0.942, outperforming all benchmark methods. These findings demonstrate that modeling higher-order customer relationships using hypergraph learning substantially improves both customer segmentation quality and purchase behavior prediction, while maintaining model transparency via explainable artificial intelligence. The proposed EHGNN framework provides an effective, robust, and interpretable solution for intelligent customer analytics and personalized decision support in modern e-commerce environments. Full article
(This article belongs to the Section Artificial Intelligence)
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28 pages, 749 KB  
Article
The Grounded-but-Wrong Gap in Cybersecurity RAG: Evidence from a Bilingual Benchmark and Multi-Judge Evaluation
by Ema Pandilova, Marko Petrov, Ivan Chorbev, Dejan Gjorgjevikj and Dimitar Trajanov
Information 2026, 17(8), 790; https://doi.org/10.3390/info17080790 - 17 Aug 2026
Viewed by 414
Abstract
Retrieval-augmented generation (RAG) is increasingly deployed in safety-critical cybersecurity question answering, where an answer must be both factually correct and faithful to its sources. Evaluation in this space routinely conflates the two, treating grounding as a proxy for correctness. We show that this [...] Read more.
Retrieval-augmented generation (RAG) is increasingly deployed in safety-critical cybersecurity question answering, where an answer must be both factually correct and faithful to its sources. Evaluation in this space routinely conflates the two, treating grounding as a proxy for correctness. We show that this conflation is unsafe. We introduce SENTINEL-QA, a bilingual English and Macedonian cybersecurity QA benchmark of 70 hand-authored items over a curated corpus of authoritative source documents, and use it to compare four answer generation pipelines (zero-shot, Classic RAG, DSPy-structured RAG, and GraphRAG) across three generators, judged by a four-model LLM panel on both factual correctness and grounding. The two metrics decouple under graph-based retrieval augmentation: on all three generators our chunk-similarity GraphRAG makes answers look better sourced without making them more correct, and as the retrieval budget grows it can even degrade factual correctness while grounding holds steady. Structured, minimal prompting improves both qualities, at lower latency and cost; a four-cell ablation attributes the factual gain to pruning the instruction, while the declarative schema contributes on grounding. The judge panel mirrors the split, agreeing more on what is well grounded than on what is true, under absolute as well as rank-based agreement criteria. We conclude that grounding alone is an insufficient target for RAG evaluation and that pruning the instruction, rather than expanding retrieval, is what improves factual correctness, without eliminating the grounded-but-wrong regime. Full article
(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)
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27 pages, 21049 KB  
Article
Cooling, Heat, Electricity and Gas Joint Load Forecasting Method Based on Modal Decomposition and Dynamic Model Selection
by He Jiang, Ruicong Han, Tianhui Shi and Yi Yang
Information 2026, 17(8), 789; https://doi.org/10.3390/info17080789 - 17 Aug 2026
Viewed by 197
Abstract
Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, and heterogeneous fluctuation characteristics, which limits the performance of conventional independent forecasting and [...] Read more.
Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, and heterogeneous fluctuation characteristics, which limits the performance of conventional independent forecasting and fixed-model approaches. To address these challenges, this study proposes a joint load forecasting framework that integrates tabular Q-learning-assisted multivariate variational mode decomposition, sample-entropy-based reconstruction, and dynamic model selection. First, tabular Q-learning is employed to select the MVMD penalty factor and the four load sequences are synchronously decomposed to preserve the coupling relationships among components with common center frequencies. Second, sample entropy is used to reconstruct the decomposed modes into high-frequency, low-frequency, and residual subsequences, thereby reducing forecasting complexity while retaining relevant temporal features. Third, a dynamic model selection mechanism evaluates SVR, BiLSTM, XGBoost, and LightGBM and assigns an appropriate predictor to each reconstructed subsequence according to its forecasting performance. The framework is evaluated using daily electricity, cooling, heating, and gas load data collected from the Tempe Campus of Arizona State University from 2016 to 2020. A rolling input window of 56 days is used to forecast the subsequent seven days. Compared with the benchmark methods, the proposed framework achieved the best overall composite performance and competitive forecasting accuracy across the four load types. These results provide a potentially useful forecasting basis for operational decision-making in integrated energy systems. Full article
(This article belongs to the Section Information Applications)
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25 pages, 9526 KB  
Article
Global Research Trends in Generative Artificial Intelligence: A Bibliometric Analysis
by Sofia Stamou and Matina Kiourexidou
Information 2026, 17(8), 788; https://doi.org/10.3390/info17080788 - 17 Aug 2026
Viewed by 417
Abstract
Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and [...] Read more.
Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape. Full article
(This article belongs to the Section Information Theory and Methodology)
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23 pages, 1332 KB  
Article
From Word Embeddings to Semantic Projections: Interpretability and Context in Web-Scale Semantic Analysis
by Mabel López-Bordao, Antonia Ferrer-Sapena, Pablo Lara-Navarra, Carlos A. Reyes-Pérez and Claudia Sánchez-Arnau
Information 2026, 17(8), 787; https://doi.org/10.3390/info17080787 - 17 Aug 2026
Viewed by 260
Abstract
Recent developments in NLP and web-scale document analysis have increasingly emphasized the importance of interpretability and contextual dependence in semantic representations. Although modern word embeddings achieve remarkable empirical performance, their semantic structure is often difficult to interpret, since meaning is encoded through latent [...] Read more.
Recent developments in NLP and web-scale document analysis have increasingly emphasized the importance of interpretability and contextual dependence in semantic representations. Although modern word embeddings achieve remarkable empirical performance, their semantic structure is often difficult to interpret, since meaning is encoded through latent geometric relations in high-dimensional spaces. This paper discusses an alternative conceptual framework based on explicit contextual semantic relations. Building on ideas from distributional semantics, co-occurrence analysis, and fuzzy set theory, the study revisits semantic projections and related count-based representations as interpretable directional semantic structures for semantic analysis in document corpora and web-based information environments. In this setting, several classical association measures, including PMI and related transformations, may be understood as derived from simpler conditional semantic projections. The methodology is illustrated through a comparative analysis of semantic associations related to “ChatGPT” across general web-scale data and specialized scientific repositories. Our results demonstrate that semantic projections effectively capture persistent contextual structures while remaining sensitive to corpus-specific discourse communities. The resulting perspective emphasizes interpretability, asymmetry, contextual dependence, and direct empirical meaning as central principles for semantic representation. Full article
(This article belongs to the Special Issue Recent Developments and Implications in Web Analysis, 2nd Edition)
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25 pages, 4145 KB  
Article
H-StreamQ: An Entity-Aware Framework for Data Quality Assessment and Drift Monitoring in Electronic Health Records
by Gul Muhammad Soomro, Zaira Hassan Amur, Said Krayem, Bronislav Chramcov, Roman Jasek and Ismail Nooraddin Ismail Allahwerdi
Information 2026, 17(8), 786; https://doi.org/10.3390/info17080786 - 17 Aug 2026
Viewed by 315
Abstract
Entity-aware quality assessment may reduce false interpretations of electronic health record (EHR) data, but evidence from small, rule-aligned benchmarks cannot establish operational effectiveness. We revised H-StreamQ as a proof-of-concept framework and evaluated its laboratory component using the complete MIMIC-IV v3.1 labevents file (158,374,764 [...] Read more.
Entity-aware quality assessment may reduce false interpretations of electronic health record (EHR) data, but evidence from small, rule-aligned benchmarks cannot establish operational effectiveness. We revised H-StreamQ as a proof-of-concept framework and evaluated its laboratory component using the complete MIMIC-IV v3.1 labevents file (158,374,764 events; 313,442 patients). Ten thousand patients were sampled across laboratory-activity quintiles and split at patient level into training (6000), threshold-calibration (2000), and test (2000) groups. The independent test set contained 918,651 numeric laboratory events. Without excluding naturally alerted records, 54,788 mutually exclusive defects were introduced using subtle value shifts, unit/scale errors, mapping errors, delayed records, and patient-clustered correlated defects. Rules, a context-aware Isolation Forest, their union (Hybrid), a context-free Isolation Forest, Local Outlier Factor (LOF), and linear and radial-basis-function (RBF) One-Class support vector machines (OCSVMs) were compared at a threshold fixed by a 2.5% calibration alert budget. Patient-cluster bootstrap intervals and event-micro and patient-macro results were reported. Rules alone achieved the highest event-micro F1-score (0.637; 95% confidence interval [CI] 0.547–0.722), followed by Hybrid (0.576; 0.484–0.668) and RBF One-Class SVM (0.559; 0.433–0.670). Hybrid increased recall over rules by only 0.004 (95% CI 0.003–0.006) while reducing F1 by 0.061 and increasing the background-alert rate by 0.015. Context conditioning did not improve aggregate Isolation Forest performance. In six batch-level drift simulations, an exponentially weighted moving average (EWMA) and a fixed-window monitor detected 97–100% and 98–100% of changes, respectively, whereas a custom Hoeffding adaptive-window detector was more conservative and often missed smaller or recurrent changes. These results support H-StreamQ as an explainable research framework, not as a validated clinical or production system. Patient-macro F1, which weights every patient equally, was substantially lower than event-micro F1 for every method (rules 0.395 versus 0.637; Hybrid 0.320 versus 0.576), indicating that event-level performance is weighted towards high-activity patients. Precision and F1 are computed relative to injected synthetic labels and are not clinically adjudicated estimates. The entity-aware architecture spans patients, admissions, diagnoses, transfers, and dictionaries, but the quantitative detection benchmark evaluates the numeric laboratory component only; other entities are used for linkage and contextual attachment and are audited descriptively rather than evaluated against labels. Full article
(This article belongs to the Special Issue Data Mining and Healthcare Informatics)
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16 pages, 1247 KB  
Article
Hierarchical Prompting with Dynamic Optimization for Knowledge Element Extraction in Fake News Detection
by Bianxia Du and Qiao Hu
Information 2026, 17(8), 785; https://doi.org/10.3390/info17080785 - 17 Aug 2026
Viewed by 266
Abstract
Fake news often manipulates fine-grained knowledge elements such as entities, events, claims, temporal expressions, attributes, and source credibility. Existing information extraction methods usually require task-specific annotations or focus on generic named entities, making them less effective for open-domain fake news scenarios where labeled [...] Read more.
Fake news often manipulates fine-grained knowledge elements such as entities, events, claims, temporal expressions, attributes, and source credibility. Existing information extraction methods usually require task-specific annotations or focus on generic named entities, making them less effective for open-domain fake news scenarios where labeled data are scarce and logical inconsistencies are subtle. This paper proposes HPDO-KEE, a hierarchical prompting framework with dynamic optimization for knowledge element extraction and feature enhancement in fake news detection. The method first defines a fake-news-oriented schema covering entities, events, claims, attribute–value pairs, relations, contradictions, and user authority. It then designs a four-layer prompt consisting of task description, core information, structure awareness, and demonstration assistance. The revised implementation distinguishes offline prompt-template rewriting from input-adaptive demonstration retrieval and automatic schema-validation retries during inference. Domain-aware demonstration selection, strict JSON constraints, redundancy removal, contradiction-candidate verification, and type correction are incorporated to improve extraction accuracy, format compliance, and stability. Experiments on CoNLL03, ACE2005, and DuEE2.0 show that HPDO-KEE achieves F1 scores of 88.9%, 82.6%, 80.3%, and 78.6% on named entity, entity, event, and Chinese event extraction tasks, respectively. Full article
(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)
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19 pages, 2480 KB  
Article
Toward Responsible GenAI-Assisted Thai Buddhist Funeral Coordination: A Pre-Deployment Acceptance Study and Evidence-to-Design Translation Framework
by Chakorn Boonprasop, Davids Makararpong and Jaratchwahn Jantarat
Information 2026, 17(8), 784; https://doi.org/10.3390/info17080784 - 16 Aug 2026
Viewed by 300
Abstract
Thai Buddhist funeral services constitute a substantial multi-actor service ecosystem that remains largely dependent on conventional, fragmented coordination among bereaved families, temples, service providers, and administrative actors, with limited digital integration. This study reports a pre-deployment acceptance evaluation of a conceptually specified GenAI-assisted [...] Read more.
Thai Buddhist funeral services constitute a substantial multi-actor service ecosystem that remains largely dependent on conventional, fragmented coordination among bereaved families, temples, service providers, and administrative actors, with limited digital integration. This study reports a pre-deployment acceptance evaluation of a conceptually specified GenAI-assisted funeral-coordination service and proposes an Evidence-to-Design Translation Framework (EDTF) for translating acceptance evidence into initial design and governance requirements. Using non-probability convenience sampling through online recruitment, 342 screened adults with prior funeral-arrangement experience in the Bangkok Metropolitan Area evaluated a standardized vignette and representative interface mockups rather than an operational platform. PLS-SEM was used to examine prospective acceptance under five predefined safeguards: traceability, auditability, contestability and redress, verification, and risk-based human review. Perceived usefulness was the strongest predictor of intention to use, while attitude toward use provided a secondary pathway. The EDTF maps empirically supported user-evaluation signals to candidate workflow features, accountability controls, required records, monitoring indicators, and escalation rules. The findings provide bounded pre-deployment evidence for subsequent prototyping and pilot validation; they do not demonstrate actual adoption, live-system effectiveness, safeguard causality, or realized industry transformation. Full article
(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)
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20 pages, 1701 KB  
Article
From Retrieval to Faithful Memory Use: Context-Grounded Evidence Chains for Music Education Agents
by Jing Mao, Yuwen Chen, Wei Wan, Kun Liu and Rui-Feng Wang
Information 2026, 17(8), 783; https://doi.org/10.3390/info17080783 - 15 Aug 2026
Viewed by 506
Abstract
Artificial intelligence systems are increasingly used for music education and music score understanding, yet most existing systems answer each task in isolation. This single-instance paradigm makes it difficult to evaluate whether an educational agent can accumulate experience, reuse prior solved cases, avoid repeated [...] Read more.
Artificial intelligence systems are increasingly used for music education and music score understanding, yet most existing systems answer each task in isolation. This single-instance paradigm makes it difficult to evaluate whether an educational agent can accumulate experience, reuse prior solved cases, avoid repeated mistakes, and make its use of memory explicit. We propose a Memory-Tool Agent with Context-Grounded Evidence Chains for music education. The agent models memory as an agent-managed tool with explicit query, organization, and verification mechanisms, while supporting broader memory operations such as update and removal in future lifelong learning settings, and it is required to construct an explicit evidence chain over retrieved memory before producing an answer. We conduct experiments on MSU-Bench using a 100-example memory pool and a 1700-example test set with DeepSeek-v4-Flash. Compared with a BM25 retrieval-augmented generation baseline, the evidence-chain pipeline improves LLM-as-Judge accuracy from 56.6% to 57.3%, raises explicit memory use to 99.6%, and increases average retrieved-memory usage from 1.5% to 75.4%. Furthermore, the non-dependence rate (answers indistinguishable from no-memory generation) is reduced from 54.7% to 8.1%, and our method demonstrates strong robustness against counterfactual memory poisoning, with only a 1.0% accuracy drop under a single poisoned memory. These results underscore the importance of explicit, verifiable memory-grounding mechanisms for building faithful AI music education agents. In the current benchmark setting, memory is initialized from previously solved examples, providing a controlled evaluation of memory-grounded reasoning rather than a full lifelong learning scenario. Full article
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20 pages, 2881 KB  
Article
Interactive Social Robot for Handwriting Learning in Early Childhood Education: Technical Evaluation via Computer Vision
by Juan E. Villegas-Cubas, Luis Otake, Oscar E. Capuñay-Uceda, Carlos Y. Valdera-Chiscol, Sttefany N. Santamaría-Oblitas and Carlos D. Jara-Huaman
Information 2026, 17(8), 782; https://doi.org/10.3390/info17080782 - 14 Aug 2026
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Abstract
Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality [...] Read more.
Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article
(This article belongs to the Special Issue Advances in Human–Robot Interactions and Assistive Applications)
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