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Volume 16
Issue 12
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Information, Volume 16, Issue 12 (December 2025) – 99 articles

Cover Story (view full-size image): Cultural tourism data is dispersed across various online sources, forming a vital asset for cultural tourism stakeholders and playing a pivotal role in decision-making and sustainability. However, collecting data from multiple origins often leads in overlapping or complementary database entries. The proposed machine learning-driven framework addresses the above via a) automated data harvesting, leveraging tailored web scrapers and focused crawlers to gather cultural information from heterogeneous sources; b) data harmonization, exploiting geodata and text similarity metrics to tackle entity resolution and integrate complementary content; and c) data augmentation, employing NLP methods—including multilingual text translation, NER, and sentiment analysis—to yield actionable insights, like cultural routes, for the cultural tourism community. View this paper
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27 pages, 1906 KB  
Article
GenIIoT: Generative Models Aided Proactive Fault Management in Industrial Internet of Things
by Isra Zafat, Arshad Iqbal, Maqbool Khan, Naveed Ahmad and Mohammed Ali Alshara
Information 2025, 16(12), 1114; https://doi.org/10.3390/info16121114 - 18 Dec 2025
Viewed by 379
Abstract
Detecting active failures is important for the Industrial Internet of Things (IIoT). The IIoT aims to connect devices and machinery across industries. The devices connect via the Internet and provide large amounts of data which, when processed, can generate information and even make [...] Read more.
Detecting active failures is important for the Industrial Internet of Things (IIoT). The IIoT aims to connect devices and machinery across industries. The devices connect via the Internet and provide large amounts of data which, when processed, can generate information and even make automated decisions on the administration of industries. However, traditional active fault management techniques face significant challenges, including highly imbalanced datasets, a limited availability of failure data, and poor generalization to real-world conditions. These issues hinder the effectiveness of prompt and accurate fault detection in real IIoT environments. To overcome these challenges, this work proposes a data augmentation mechanism which integrates generative adversarial networks (GANs) and the synthetic minority oversampling technique (SMOTE). The integrated GAN-SMOTE method increases minority class data by generating failure patterns that closely resemble industrial conditions, increasing model robustness and mitigating data imbalances. Consequently, the dataset is well balanced and suitable for the robust training and validation of learning models. Then, the data are used to train and evaluate a variety of models, including deep learning architectures, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), and conventional machine learning models, such as support vector machines (SVMs), K-nearest neighbors (KNN), and decision trees. The proposed mechanism provides an end-to-end framework that is validated on both generated and real-world industrial datasets. In particular, the evaluation is performed using the AI4I, Secom and APS datasets, which enable comprehensive testing in different fault scenarios. The proposed scheme improves the usability of the model and supports its deployment in a real IIoT environment. The improved detection performance of the integrated GAN-SMOTE framework effectively addresses fault classification challenges. This newly proposed mechanism enhances the classification accuracy up to 0.99. The proposed GAN-SMOTE framework effectively overcomes the major limitations of traditional fault detection approaches and proposes a robust, scalable and practical solution for intelligent maintenance systems in the IIoT environment. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: “Information Processes”, 2nd Edition)
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13 pages, 1312 KB  
Article
Exploring the Role of Augmented Reality in STEAM Learning Environments: Evidence from Geometry Education
by Alban Gjoka and Krenare Pireva Nuci
Information 2025, 16(12), 1113; https://doi.org/10.3390/info16121113 - 18 Dec 2025
Viewed by 550
Abstract
Technology plays an increasingly vital role in modern education, providing new opportunities to enhance engagement and conceptual understanding. Among emerging innovations, Augmented Reality (AR) enables interactive visualization that supports deeper comprehension of abstract and spatially complex concepts. This study aimed to evaluate the [...] Read more.
Technology plays an increasingly vital role in modern education, providing new opportunities to enhance engagement and conceptual understanding. Among emerging innovations, Augmented Reality (AR) enables interactive visualization that supports deeper comprehension of abstract and spatially complex concepts. This study aimed to evaluate the impact of AR technology integrated with the STEAM approach on fifth-grade students’ learning of geometric solids, focusing on spatial skills, motivation, and academic achievement. A quasi-experimental design was implemented, involving an experimental group that engaged in AR- and STEAM-based activities and a control group that followed traditional instruction. Results indicated significant improvement in geometry test performance within the experimental group (p < 0.001) and higher post-test performance compared to the control group (p = 0.005). Although motivation scores were higher in the experimental group, the difference was not statistically significant (p = 0.083), suggesting a positive trend that merits further exploration with a larger sample. Overall, the findings highlight the pedagogical potential of integrating AR and STEAM approaches to support engagement and conceptual understanding in geometry education. Full article
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19 pages, 4278 KB  
Article
Research on Transfer Learning-Based Fault Diagnosis for Planetary Gearboxes Under Cross-Operating Conditions via IDANN
by Xiaolu Wang, Aiguo Wang, Haoyu Sun and Xin Xia
Information 2025, 16(12), 1112; https://doi.org/10.3390/info16121112 - 18 Dec 2025
Viewed by 267
Abstract
To address the limited performance of transfer fault diagnosis for planetary gearboxes under cross-operating conditions, which is caused by the heterogeneous feature distribution of vibration data and insufficient feature extraction. An improved domain-adversarial neural network (IDANN) model based on a joint-adaptive-domain alignment component [...] Read more.
To address the limited performance of transfer fault diagnosis for planetary gearboxes under cross-operating conditions, which is caused by the heterogeneous feature distribution of vibration data and insufficient feature extraction. An improved domain-adversarial neural network (IDANN) model based on a joint-adaptive-domain alignment component and a dual-branch feature extractor is proposed. Firstly, a joint domain adaptation alignment approach, integrating maximum mean discrepancy (MMD) and CORrelation ALignment (CORAL), is proposed to realize the correlation structure matching of features between the source and target domains of IDANN. Secondly, a dual-branch feature extractor composed of ResNet18 and Swin Transformer is proposed with an attention-weighted fusion mechanism to enhance feature extraction. Finally, validation experiments conducted on public planetary gearbox fault datasets show that the proposed method attains high accuracy and stable performance in cross-operating-condition transfer fault diagnosis. Full article
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27 pages, 3823 KB  
Article
Experiences Regarding Anonymising and Publishing Personal Data as Open Data in Germany: Results of an Online Survey
by Norbert Lichtenauer, Lukas Schmidbauer, Florian Wahl and Sebastian Wilhelm
Information 2025, 16(12), 1111; https://doi.org/10.3390/info16121111 - 17 Dec 2025
Viewed by 290
Abstract
Introduction: The anonymisation of Personal Data (PD) and its release as Open Data (OD) hold considerable potential for innovation across health, research, public administration, and the economy. However, practical experiences regarding data anonymisation and OD publication remain underexplored in Germany. This study empirically [...] Read more.
Introduction: The anonymisation of Personal Data (PD) and its release as Open Data (OD) hold considerable potential for innovation across health, research, public administration, and the economy. However, practical experiences regarding data anonymisation and OD publication remain underexplored in Germany. This study empirically investigates the current state of anonymised data practices, the barriers to implementation, and the desired support mechanisms for publishing formerly PD as OD. Methods: Embedded in a mixed-methods approach, this cross-sectional study examines research interest in the collection, processing, and use of anonymised data, as well as potential barriers and support services for the anonymisation and publication of former PD. A nationwide online survey was conducted in October–November 2024 via LimeSurvey. A total of 215 responses were included in the descriptive analysis. Results: The findings indicate limited experience with PD anonymisation and OD publication across industries. The potential added value of these processes was often not fully recognised, and data-handling responsibilities were rarely standardised. Data collectors, data protection officers, and IT departments were identified as the most frequently involved parties in these processes. Technical and educational support were the most desired forms of assistance. Discussion: To foster broader OD utilisation, stakeholders require comprehensive support. According to the sample, specific training and further education on the anonymisation and publishing process, as well as the desired software, are most important. Developing standardised process descriptions that integrate ethical and legal considerations, supported by national networks or governmental institutions, could significantly enhance the responsible and effective use of anonymised OD in Germany. Full article
(This article belongs to the Section Information Security and Privacy)
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23 pages, 1623 KB  
Article
An Empirical Case Study of Digital Government Transformation in Saudi Arabia
by Sara Alkorbi and Omer Alrwais
Information 2025, 16(12), 1110; https://doi.org/10.3390/info16121110 - 17 Dec 2025
Viewed by 933
Abstract
Digital transformation has emerged as a key driver of modernization in the private and public sectors. In recent years, governments worldwide have turned to digital technologies to improve efficiency, reduce costs, and enhance citizen engagement. Saudi Arabia, through Vision 2030, launched one of [...] Read more.
Digital transformation has emerged as a key driver of modernization in the private and public sectors. In recent years, governments worldwide have turned to digital technologies to improve efficiency, reduce costs, and enhance citizen engagement. Saudi Arabia, through Vision 2030, launched one of the most ambitious national digital transformation programs, aiming to reposition the country as a leading digital government. The Saudi government initiated a wide range of digital initiatives across ministries, agencies, and public institutions—marking a critical period of structural, technological, and cultural change in the public sector. Despite the scale and significance of this transformation, academic research on Saudi Arabia’s DT efforts remains limited. Most available insights are derived from media reports, conference presentations, or informal commentary, with minimal empirical evaluation. This study addresses that gap by conducting a comprehensive qualitative case study to assess the progress, challenges, and outcomes of digital government transformation in Saudi Arabia during the 2017–2020 period. This research examines digital transformation in the public sector of an emerging economy. It highlights three essentials: institutional coordination, systems to track progress, and long-term investment in digital skills and infrastructure. The researcher interviewed staff from the digital unit and ministry teams, conducted fieldwork, and analyzed official documents and websites. The findings indicate substantial progress in digitizing public services and enhancing user access. However, persistent challenges remain, particularly in data integration, policy alignment, and inter-agency collaboration. Full article
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25 pages, 1232 KB  
Article
DLF: A Deep Active Ensemble Learning Framework for Test Case Generation
by Yaogang Lu, Yibo Peng and Dongqing Zhu
Information 2025, 16(12), 1109; https://doi.org/10.3390/info16121109 - 16 Dec 2025
Viewed by 217
Abstract
High-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model predictions. These limitations make them prone to local optima in complex or [...] Read more.
High-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model predictions. These limitations make them prone to local optima in complex or cross-scenario tasks and hinder their ability to balance testing quality with execution efficiency. To address these challenges, this paper proposes a Deep Active Ensemble Learning Framework for symbolic execution path exploration. During training, the framework integrates active learning with ensemble learning to reduce annotation costs and improve model robustness, while constructing a heterogeneous model pool to leverage complementary model strengths. In the testing stage, a dynamic ensemble mechanism based on sample similarity adaptively selects the optimal predictive model to guide symbolic path exploration. In addition, a gated graph neural network is employed to extract structural and semantic features from the control flow graph, improving program behavior understanding. To balance efficiency and coverage, a dynamic sliding window mechanism based on branch density enables real-time window adjustment under path complexity awareness. Experimental results on multiple real-world benchmark programs show that the proposed framework detects up to 16 vulnerabilities and achieves a cumulative 27.5% increase in discovered execution paths in hybrid fuzzing. Furthermore, the dynamic sliding window mechanism raises the F1 score to 93%. Full article
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20 pages, 1206 KB  
Article
Neural Network-Based Capital Management for Bitcoin Trading: A Risk-Aware Expert System for Investment Strategy Optimization
by Pedro Gabana and Matilde Santos
Information 2025, 16(12), 1108; https://doi.org/10.3390/info16121108 - 16 Dec 2025
Viewed by 389
Abstract
This study presents an expert system designed to generate Bitcoin investment strategies based on cryptocurrency market indicators. Historical BTC daily closing prices from 2015 to 2021 were processed to build the system’s predictive foundation. Multilayer perceptron (MLP) neural networks with various configurations were [...] Read more.
This study presents an expert system designed to generate Bitcoin investment strategies based on cryptocurrency market indicators. Historical BTC daily closing prices from 2015 to 2021 were processed to build the system’s predictive foundation. Multilayer perceptron (MLP) neural networks with various configurations were then employed to forecast both price levels and directional movements in Bitcoin’s value. These networks were trained using supervised learning techniques and assessed through multiple evaluation metrics. The configuration achieving the lowest RMSE and highest trend prediction accuracy was subsequently used to implement a capital management system capable of executing long, short, and combined trading positions in the Bitcoin market. An all-or-nothing investment scheme was applied and benchmarked against a traditional Buy & Hold (B&H) strategy. The proposed system achieved up to +68% profitability in the combined long/short configuration while reducing maximum drawdown by more than 40%. In addition, an expert supervisory layer was integrated, incorporating market indicators such as stop-loss, take-profit, and market withdrawal rules based on maximum adverse excursion (MAE) and maximum favorable excursion (MFE). Although this supervisory layer slightly reduced profitability in some scenarios, it enhanced risk control and capital protection during highly volatile periods. Overall, the proposed framework demonstrates that neural network–driven trading strategies, when combined with supervisory expert rules, can significantly outperform a passive Buy & Hold approach, offering a reproducible and fully automated solution for Bitcoin capital management. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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23 pages, 994 KB  
Article
Will IP Location Openness Affect Posts?—An Empirical Examination from Sina Weibo
by Zhong Wang, Weili Huang, Xinxian Pan and Weihong Xie
Information 2025, 16(12), 1107; https://doi.org/10.3390/info16121107 - 15 Dec 2025
Viewed by 394
Abstract
A few countries have requested open IP locations of posters in order to combat rumors and strengthen management. Such policies intensify information surveillance of users, which may in turn influence their online behavior. In the context of multiple governments considering the implementation of [...] Read more.
A few countries have requested open IP locations of posters in order to combat rumors and strengthen management. Such policies intensify information surveillance of users, which may in turn influence their online behavior. In the context of multiple governments considering the implementation of this policy, it is essential to assess its impact. We examine the impact of IP location openness on posters’ behavior and patterns based on the empirical data of Sina Weibo, and analyze the heterogeneous impact on users of different genders. Regression discontinuity and short-run panel data regression results show that IP location openness reduces the frequency of users’ social media participation behavior; specifically, the frequency of reposting microblogs and posting geo-tagged microblogs is remarkably diminished, while the frequency of posting photos is not discernibly changed. Long-run panel data regression results indicate that the overall inhibitory effect on the frequency of social media participation behavior disappears, and it only has a negative effect on posting geo-tagged microblogs. The results of heterogeneity analysis suggest that the short-run negative impact of IP location openness on female users’ social media participation behavior is more remarkable than that of male users. Full article
(This article belongs to the Special Issue Digital Technology and Cyber Security)
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18 pages, 2485 KB  
Article
Adaptive Token Boundaries: Towards Integrating Human Chunking Mechanisms into Multimodal LLMs
by Dongxing Yu
Information 2025, 16(12), 1106; https://doi.org/10.3390/info16121106 - 15 Dec 2025
Viewed by 323
Abstract
Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimodal information integration. This research presents a systematic investigation into the parallels between human [...] Read more.
Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimodal information integration. This research presents a systematic investigation into the parallels between human cross-modal chunking mechanisms and token representation methodologies in MLLMs. Through empirical studies comparing human performance patterns with model behaviors across visual–linguistic tasks, we demonstrate that conventional static tokenization schemes fundamentally constrain current models’ capacity to simulate the dynamic, context-sensitive nature of human information processing. We propose a novel framework for dynamic cross-modal tokenization that incorporates adaptive boundaries, hierarchical representations, and alignment mechanisms grounded in cognitive science principles. Quantitative evaluations demonstrate that our approach yields statistically significant improvements over state-of-the-art models on benchmark tasks (+7.8% on Visual Question Answering (p < 0.001), 5.3% on Complex Scene Description) while exhibiting more human-aligned error patterns and attention distributions. These findings contribute to the theoretical understanding of the relationship between human cognition and artificial intelligence, while providing empirical evidence for developing more cognitively plausible AI systems. Full article
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22 pages, 1236 KB  
Article
An Industrial Framework for Cold-Start Recommendation in Few-Shot and Zero-Shot Scenarios
by Xulei Cao, Wenyu Zhang, Feiyang Jiang and Xinming Zhang
Information 2025, 16(12), 1105; https://doi.org/10.3390/info16121105 - 15 Dec 2025
Viewed by 432
Abstract
With the rise of online advertising, e-commerce industries, and new media platforms, recommendation systems have become an essential product form that connects users with a vast number of candidates. A major challenge in recommendation systems is the cold-start problem, where the absence of [...] Read more.
With the rise of online advertising, e-commerce industries, and new media platforms, recommendation systems have become an essential product form that connects users with a vast number of candidates. A major challenge in recommendation systems is the cold-start problem, where the absence of historical interaction data for new users and items leads to poor recommendation performance. We first analyze the causes of the cold-start problem, highlighting the limitations of existing embedding models when faced with a lack of interaction data. To address this, we classify the features of models into three categories, leveraging the Trans Block mapping to transfer features into the semantic space of missing features. Then, we propose a model-agnostic industrial framework (MAIF) with the Auto-Selection serving mechanism to address the cold-start recommendation problem in few-shot and zero-shot scenarios without requiring training from scratch. This framework can be applied to various online models without altering the prediction for warm entities, effectively avoiding the “seesaw phenomenon” between cold and warm entities. It improves prediction accuracy and calibration performance in three cold-start scenarios of recommendation systems. Finally, both the offline experiments on real-world industrial datasets and the online advertising system on the Dazhong Dianping app validate the effectiveness of our approach, showing significant improvements in recommendation performance for cold-start scenarios. Full article
(This article belongs to the Special Issue AI and Machine Learning in the Big Data Era: Advanced Algorithms and Real-World Applications)
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14 pages, 448 KB  
Article
PLSSEM Comparison Study of Mobile Payment Usage in Hong Kong and Mainland China: Factors Affecting the Popularity of Mobile Payment
by Woonkwan Tse, Pulei Liu, Zongbin Ouyang, Mingshan Li and Haoming Wen
Information 2025, 16(12), 1104; https://doi.org/10.3390/info16121104 - 15 Dec 2025
Viewed by 325
Abstract
As a financial center of Asia, Hong Kong has been the leading edge of fintech innovation, with the a leading ranking of the Global Innovation Index, which only ranked the fifth among all the payment methods in 2023 whereas mainland China achieved 90% [...] Read more.
As a financial center of Asia, Hong Kong has been the leading edge of fintech innovation, with the a leading ranking of the Global Innovation Index, which only ranked the fifth among all the payment methods in 2023 whereas mainland China achieved 90% acceptance in 2018. Since Hong Kong is part of China and shares similar origins and cultures, we found the need to study consumer behaviors in both of the two regions. We use comparison study methodology to find out the reasons of the difference in the usage. This research aims to investigate the factors influencing the acceptance of mobile payment services in Hong Kong and its difference in mainland China. In this research, we use the Partial Least Square Structural Equation Modeling methodology which discovers several significant factors influencing the actual use of mobile payment systems in Hong Kong and mainland China and tries to explain this. The findings will contribute to a better understanding of user behaviors and preferences, assisting stakeholders to address the challenges, develop effective strategies to increase the acceptance and use of mobile payment services, and promote payment convenience in Hong Kong. Full article
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28 pages, 2880 KB  
Article
A Novel Hybrid GWO-RFO Metaheuristic Algorithm for Optimizing 1D-CNN Hyperparameters in IoT Intrusion Detection Systems
by Eslam Bokhory Elsayed, Abdalla Sayed Yassin and Hanan Fahmy
Information 2025, 16(12), 1103; https://doi.org/10.3390/info16121103 - 15 Dec 2025
Viewed by 320
Abstract
Because Internet of Things (IoT) networks are widely deployed, they have become attractive targets for botnet and distributed denial of service (DDoS) attacks, which require effective intrusion detection. Convolutional neural networks (CNNs) can achieve strong detection performance, but their many hyperparameters are usually [...] Read more.
Because Internet of Things (IoT) networks are widely deployed, they have become attractive targets for botnet and distributed denial of service (DDoS) attacks, which require effective intrusion detection. Convolutional neural networks (CNNs) can achieve strong detection performance, but their many hyperparameters are usually tuned manually, which is costly and time-consuming. This paper proposes a new hybrid metaheuristic optimizer, FW-CNN, that combines Grey Wolf Optimization and Red Fox Optimization to automatically tune the key hyperparameters of a one-dimensional CNN for IoT intrusion detection. The Red Fox component enhances exploration and helps the search escape local optima, while the Grey Wolf component strengthens exploitation and guides convergence toward high-quality solutions. The proposed model is evaluated using the N-BaIoT dataset and compared with a feedforward neural network as well as a metaheuristic-optimized model based on the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-CNN. It achieves a final accuracy of 95.56%, improving on the feedforward network by 12.56 percentage points and outperforming the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-based CNN model by 1.02 percentage points. It also yields higher average precision, Kappa coefficient, and Jaccard similarity, and significantly reduces Hamming loss. These results indicate that the proposed hybrid optimizer is stable and effective for multi-class IoT intrusion detection in real environments. Full article
(This article belongs to the Special Issue Security and Privacy of Resource-Constrained IoT Devices)
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21 pages, 2975 KB  
Article
FFM-Net: Fusing Frequency Selection Information with Mamba for Skin Lesion Segmentation
by Lifang Chen, Entao Yu, Qihang Cao and Ke Hu
Information 2025, 16(12), 1102; https://doi.org/10.3390/info16121102 - 13 Dec 2025
Viewed by 312
Abstract
Accurate segmentation of lesion regions is essential for skin cancer diagnosis. As dermoscopic images of skin lesions demonstrate different sizes, diverse shapes, fuzzy boundaries, and so on, accurate segmentation still faces great challenges. To address these issues, we propose a new dermatologic image [...] Read more.
Accurate segmentation of lesion regions is essential for skin cancer diagnosis. As dermoscopic images of skin lesions demonstrate different sizes, diverse shapes, fuzzy boundaries, and so on, accurate segmentation still faces great challenges. To address these issues, we propose a new dermatologic image segmentation network, FFM-Net. In FFM-Net, we design a new FM block encoder based on state space models (SSMs), which integrates a low-frequency information extraction module (LEM) and an edge detail extraction module (EEM) to extract broader overall structural information and more accurate edge detail information, respectively. At the same time, we dynamically adjust the input channel ratios of the two module branches at different stages of our network, so that the model can learn the correlation relationship between the overall structure and edge detail features more effectively. Furthermore, we designed the cross-channel spatial attention (CCSA) module to improve the model’s sensitivity to channel and spatial dimensions. We deploy a multi-level feature fusion module (MFFM) at the bottleneck layer to aggregate rich multi-scale contextual representations. Finally, we conducted extensive experiments on three publicly available skin lesion segmentation datasets, ISIC2017, ISIC2018, and PH2, and the experimental results show that the FFM-Net model outperforms most existing skin lesion segmentation methods. Full article
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22 pages, 1857 KB  
Article
Human–AI Learning: Architecture of a Human–AgenticAI Learning System
by Peter Williams
Information 2025, 16(12), 1101; https://doi.org/10.3390/info16121101 - 12 Dec 2025
Viewed by 593
Abstract
The Ancient Greeks foresaw non-human automata and the power of dialogic learning, but Generative AI and AgenticAI afford the prospect of going beyond interlocutor to co-creator in an empowering partnership between learner and AI agent to address ‘whole person’ education. This exploratory study [...] Read more.
The Ancient Greeks foresaw non-human automata and the power of dialogic learning, but Generative AI and AgenticAI afford the prospect of going beyond interlocutor to co-creator in an empowering partnership between learner and AI agent to address ‘whole person’ education. This exploratory study reviews existing conceptual models and implementations of learning with AI before proposing the novel and original architecture of a human–AgenticAI learning system. In this, the learner and human tutor are each supported by AI assistants, and an AI tutor coordinates the generation, presentation and assessment of adaptive learning activities requiring the partnership of learner and AI assistant in the co-creation of learning outcomes. The proposed model is significant for incorporating 21st-century skills in a diversity of realistic learning environments. It tracks a formative assessment pathway of the learner’s contribution to co-created outcomes through to the compilation of a summative achievement portfolio for external warranting. Although focused upon learning in universities, the model is transferable to other educational milieux. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
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24 pages, 636 KB  
Article
The Relationship Between Information Technology Dimensions and Competitiveness Dimensions of SMEs Mediated by the Role of Innovative Performance
by AmirHossein ArminKia, Mahdi Moradi and Mahdi Salehi
Information 2025, 16(12), 1100; https://doi.org/10.3390/info16121100 - 11 Dec 2025
Viewed by 354
Abstract
This study evaluated the relationship between information technology (IT) and competitiveness (CP), emphasizing the different dimensions of IT capabilities, including customer relationship management (CRM) and human resource management (HRM). Also, the mediating role of innovative performance (IP) was examined in the link between [...] Read more.
This study evaluated the relationship between information technology (IT) and competitiveness (CP), emphasizing the different dimensions of IT capabilities, including customer relationship management (CRM) and human resource management (HRM). Also, the mediating role of innovative performance (IP) was examined in the link between IT use and CP. Data were collected in 2023 through a standard questionnaire, whose validity and reliability were confirmed by experts and statistical tests. Then, 172 valid responses were analyzed after determining the minimum sample size using Cochran’s formula. SPSS version 25 was used for descriptive analyses and preliminary tests, while SmartPLS 3.3.3 was utilized for structural equation modeling and hypothesis testing. The findings indicated that the use of IT components enhances CP, and IP mediates this relationship. This research contributes to the theoretical development of innovation management and IT by highlighting the transmission mechanism of IP rather than focusing solely on the direct relationship. This study, conducted among Iranian small and medium-sized enterprises (SMEs), also fills a gap in global literature, especially in developing countries, and offers practical insights. Full article
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21 pages, 2587 KB  
Article
Improving Avatar Accuracy with Gaussian Process Regression Method in Mirror Metaverses
by Mai Cong Huong, Artem Volkov, Ammar Muthanna, Andrey Koucheryavy, Dmitry Kozyrev and János Sztrik
Information 2025, 16(12), 1099; https://doi.org/10.3390/info16121099 - 11 Dec 2025
Viewed by 534
Abstract
This paper deals with unwanted spatial distortion in virtual environments and its impact on the construction of metaverse environments that require high precision, especially in fields with specific requirements, such as medicine. At the same time, it presents the main technical factors leading [...] Read more.
This paper deals with unwanted spatial distortion in virtual environments and its impact on the construction of metaverse environments that require high precision, especially in fields with specific requirements, such as medicine. At the same time, it presents the main technical factors leading to this phenomenon. The paper also emphasizes that data reliability is the first factor that needs to be analyzed and evaluated. Through a comprehensive analysis of the limitations of traditional methods and the development trend of techniques based on Artificial Intelligence (AI), a data processing method based on the Gaussian process regression method is proposed. Through experiments and result analysis, this method significantly improves data reliability, thereby enhancing the accuracy of avatar motion simulation in the virtual environment of the metaverse. Future research trends include further improvement of processing accuracy and speed; deploying on real devices; expanding the research into other factors contributing to unintended spatial distortions; exploring and applying appropriate processing techniques and technologies to enhance simulation reliability in virtual metaverse environments. Full article
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16 pages, 452 KB  
Article
Validating the Use of Natural Language Processing and Text Mining for Hospital-Based Violence Intervention Programs and Criminal Justice Articles
by Cyril S. Ku, Katheryne Pugliese, Jared R. Dmello, Morgan R. Peltier, Robert Green and Sheetal Ranjan
Information 2025, 16(12), 1098; https://doi.org/10.3390/info16121098 - 11 Dec 2025
Viewed by 353
Abstract
Hospital-based violence intervention programs (HVIPs) are a form of community violence intervention designed to address trauma resulting from violent injuries. This public health approach has been implemented across the United States since the 1990s, with numerous qualitative and quantitative studies evaluating its effectiveness. [...] Read more.
Hospital-based violence intervention programs (HVIPs) are a form of community violence intervention designed to address trauma resulting from violent injuries. This public health approach has been implemented across the United States since the 1990s, with numerous qualitative and quantitative studies evaluating its effectiveness. Manual systematic reviews by domain experts have helped identify major themes and research gaps. While these reviews are valuable for synthesizing the existing literature, thisprocess can be time-consuming and labor-intensive, given the vast amount of research in public health and criminal justice. To meet the urgent need for accessible insights into the violence-related literature, more efficient methods are essential. Recent advances in artificial intelligence (AI) offer promising tools to streamline this process. This study applies AI, specifically natural language processing techniques, to analyze recurring themes in the HVIP-related literature at the intersection of criminal justice and public health. The findings indicate that text-mining methods can enhance and accelerate the systematic review process, while also revealing new insights. The results underscore the potential of AI-driven tools to support evidence-based practices and highlight the importance of interdisciplinary collaboration to improve the effectiveness and implementation of HVIPs. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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19 pages, 2315 KB  
Article
Client-Attentive Personalized Federated Learning for AR-Assisted Information Push in Power Emergency Maintenance
by Cong Ye, Xiao Li, Zile Lei, Jianlei Wang, Tao Zhang and Sujie Shao
Information 2025, 16(12), 1097; https://doi.org/10.3390/info16121097 - 11 Dec 2025
Viewed by 226
Abstract
The integration of AI into power emergency maintenance faces a critical dilemma: centralized training compromises privacy, while standard Federated Learning (FL) struggles with the statistical heterogeneity (Non-IID) of industrial data. Traditional aggregation algorithms (e.g., FedAvg) treat clients solely based on sample size, failing [...] Read more.
The integration of AI into power emergency maintenance faces a critical dilemma: centralized training compromises privacy, while standard Federated Learning (FL) struggles with the statistical heterogeneity (Non-IID) of industrial data. Traditional aggregation algorithms (e.g., FedAvg) treat clients solely based on sample size, failing to distinguish between critical fault data and redundant normal operational data. To address this theoretical gap, this paper proposes a Client-Attentive Personalized Federated Learning (PFAA) framework. Unlike conventional approaches, PFAA introduces a semantic-aware attention mechanism driven by “Device Health Fingerprints.” This mechanism dynamically quantifies the contribution of each client not just by data volume, but by the quality and physical relevance of their model updates relative to the global optimization objective. We implement this algorithm within a collaborative cloud-edge-end architecture to enable privacy-preserving, AR-assisted fault diagnosis. Extensive simulations demonstrate that PFAA effectively mitigates model divergence caused by data heterogeneity, achieving superior convergence speed and decision accuracy compared to rule-based and standard FL baselines. Full article
(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)
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32 pages, 2022 KB  
Article
A Data-Driven Topic Modeling Analysis of Blockchain in Food Supply Chain Traceability
by Abderahman Rejeb, Karim Rejeb, Homa Molavi and John G. Keogh
Information 2025, 16(12), 1096; https://doi.org/10.3390/info16121096 - 10 Dec 2025
Viewed by 633
Abstract
Blockchain technology plays a critical role in strengthening traceability in food supply chains (FSCs), particularly in relation to transparency, authenticity, food safety, and sustainability. This study conducts a systematic review of 518 journal articles retrieved from Scopus and Web of Science and applies [...] Read more.
Blockchain technology plays a critical role in strengthening traceability in food supply chains (FSCs), particularly in relation to transparency, authenticity, food safety, and sustainability. This study conducts a systematic review of 518 journal articles retrieved from Scopus and Web of Science and applies latent Dirichlet allocation (LDA) topic modeling to identify dominant research trends. The analysis reveals eight key themes, including blockchain adoption enablers and challenges, consumer perceptions, supply chain traceability systems, sustainability, and food safety applications. The findings highlight significant growth in academic interest and demonstrate how blockchain improves visibility and efficiency across supply chain actors. The review offers theoretical insights into blockchain’s interdisciplinary role in FSC traceability and provides practical guidance for farmers, food industries, policymakers, and technology developers, while outlining future research opportunities. Full article
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37 pages, 3305 KB  
Systematic Review
AI-Assisted OSINT/SOCMINT for Safeguarding Borders: A Systematic Review
by Alexandros Karakikes and Konstantinos Kotis
Information 2025, 16(12), 1095; https://doi.org/10.3390/info16121095 - 10 Dec 2025
Viewed by 1168
Abstract
In the highly volatile realm of global security, the necessity for leading-edge and effectual border resilience tactics has never been more imperative. This PRISMA 2020 guided systematic literature review (SLR) examines the intersection of artificial intelligence (AI), open-source intelligence (OSINT), and social media [...] Read more.
In the highly volatile realm of global security, the necessity for leading-edge and effectual border resilience tactics has never been more imperative. This PRISMA 2020 guided systematic literature review (SLR) examines the intersection of artificial intelligence (AI), open-source intelligence (OSINT), and social media intelligence (SOCMINT) for enhancing border protection. Our systematic investigation across major databases (IEEE Xplore, Scopus, SpringerLink, MDPI, ACM) and grey literature sources yielded 3932 initial records and, after screening and eligibility assessment, 73 studies and reports from acknowledged organizations, contributing to the evidence synthesis. Three research questions (RQ1–RQ3) were addressed concerning the following: (a) the effectiveness and application of AI in OSINT/SOCMINT for border protection, its (b) data, technical, and operational limitations, and its (c) ethical, legal, and societal implications (GELSI). Evidence matrices summarize the findings, while narrative syntheses underline and thematically group the extracted insights. Results indicate that AI techniques—fluctuating from machine learning (ML) and natural language processing (NLP) to computer vision and emerging large language models (LLMs)—produce quantifiable improvements in forecasting irregular migration, detecting human trafficking, and supporting multimodal intelligence fusion. However, limitations include misinformation, data bias, adversarial vulnerabilities, governance deficits, and sandbox-to-production gaps. Ethical and societal concerns highlight risks of surveillance overreach, discrimination, and insufficient oversight, among others. To our knowledge, this is the first SLR at this intersection. We conclude that, AI-assisted OSINT/SOCMINT presents transformative potential for border protection requiring, nonetheless, balanced governance, robust validation, and future research on LLM/agentic AI, human–AI teaming, and oversight mechanisms. Full article
(This article belongs to the Special Issue Complex Network Analysis in Security)
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23 pages, 18052 KB  
Review
Gamification in Learning Management Systems: A Systematic Literature Review
by Georgios Lampropoulos, Boishakhi Ghosh Mukta and Theofylaktos Anastasiadis
Information 2025, 16(12), 1094; https://doi.org/10.3390/info16121094 - 10 Dec 2025
Viewed by 877
Abstract
Gamification and learning management systems (LMSs) are increasingly being used across educational levels. Hence, the adoption of gamified LMSs, that is, LMSs that integrate gamification elements, is also gaining ground due to the potential benefits they can yield. This study aims to examine [...] Read more.
Gamification and learning management systems (LMSs) are increasingly being used across educational levels. Hence, the adoption of gamified LMSs, that is, LMSs that integrate gamification elements, is also gaining ground due to the potential benefits they can yield. This study aims to examine the integration of gamification into LMSs through a systematic literature review by exploring 139 related studies from Scopus, Web of Science, and IEEE that were published from 2013 to 2025. This study focuses on identifying the most prominent gamification elements and the main implications in terms of benefits and challenges. Based on the outcomes, gamified LMSs can positively affect the overall educational process. Specifically, gamified LMSs showcase great potential to improve the educational process, support education stakeholders, provide meaningful learning opportunities, satisfy students’ innate needs, and increase their learning outcomes, including academic performance, motivation, engagement, interest, enjoyment, and satisfaction. Gamified LMSs can enrich existing teaching and learning practices and are positively assessed by education stakeholders. They can improve students’ self-regulated learning and satisfy their innate needs for autonomy, relatedness, and competences. They support social and collaborative learning, foster a sense of accomplishment, and provide new methods of assessment and new metrics to analyze students’ learning. However, the effectiveness of gamified LMSs is ultimately determined by the quality of their design and the extent to which gamification strategies and activities are pedagogically grounded and appropriately aligned with the learning goals, population, and context. Full article
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29 pages, 895 KB  
Article
The Feasibility and Acceptability of AI-Based eGuide for Healthcare Centers in Oman
by Yasir Abdelgadir Mohamed, Mohamed Bashir, Akbar Khanan and Dil Nawaz Hakro
Information 2025, 16(12), 1093; https://doi.org/10.3390/info16121093 - 10 Dec 2025
Viewed by 455
Abstract
The rapid advancement of artificial intelligence (AI) in healthcare delivery has introduced innovative tools to improve patient care, streamline administrative processes, and bridge accessibility gaps. This study assesses how end-users perceive the practicality and usability of a proposed AI-enabled eGuide within Omani healthcare [...] Read more.
The rapid advancement of artificial intelligence (AI) in healthcare delivery has introduced innovative tools to improve patient care, streamline administrative processes, and bridge accessibility gaps. This study assesses how end-users perceive the practicality and usability of a proposed AI-enabled eGuide within Omani healthcare facilities, addressing cultural, linguistic, and regulatory requirements unique to the Sultanate. Through a mixed-methods framework combining stakeholder analysis, technological readiness assessment, and socio-cultural adaptation strategies, the research identifies the operational, economic, and ethical viability of the system. The current research results suggest that regulatory alignment, stakeholder engagement, and proper localization of AI-based eGuides will significantly enhance patient navigation after being tested on a wider dataset or real-world healthcare environments, reduce healthcare delivery bottlenecks, and increase patient satisfaction. Furthermore, digital literacy disparities, data privacy compliance, and infrastructure variability challenges need to be planned strategically and handled with care. This study offers a roadmap for policymakers and healthcare administrators to adopt AI-enabled eGuide systems that are both technically feasible and socially acceptable within the Omani healthcare ecosystem. Full article
(This article belongs to the Special Issue Advancements in Healthcare Data Science: Innovations, Challenges and Applications)
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25 pages, 1219 KB  
Article
Chain-of-Thought Prompt Optimization via Adversarial Learning
by Guang Yang, Xiantao Cai, Shaohe Wang and Juhua Liu
Information 2025, 16(12), 1092; https://doi.org/10.3390/info16121092 - 9 Dec 2025
Viewed by 874
Abstract
Chain-of-Thought (CoT) prompting has demonstrated strong effectiveness in improving the reasoning capabilities of Large Language Models (LLMs). However, existing CoT optimization approaches still lack systematic mechanisms for evaluating and refining prompts. To address this gap, we propose Adversarial Chain-of-Thought (adv-CoT), a framework that [...] Read more.
Chain-of-Thought (CoT) prompting has demonstrated strong effectiveness in improving the reasoning capabilities of Large Language Models (LLMs). However, existing CoT optimization approaches still lack systematic mechanisms for evaluating and refining prompts. To address this gap, we propose Adversarial Chain-of-Thought (adv-CoT), a framework that introduces adversarial learning into prompt optimization. Adv-CoT iteratively refines an initial prompt through generator–discriminator interactions and integrates both feedback and verification mechanisms. This process enables more targeted and interpretable improvements to CoT instructions and demonstrations. We evaluate adv-CoT on twelve datasets across commonsense, factual, symbolic, and arithmetic reasoning. Across 12 reasoning datasets, adv-CoT yields an average improvement of 4.44% on GPT-3.5-turbo and 1.08% on GPT-4o-mini, with both gains being statistically significant (paired t-test, p < 0.05). The experimental results show that the framework yields consistent but task-dependent gains, particularly on numerical and factual reasoning tasks, and maintains competitive performance on symbolic and commonsense benchmarks. Paired significance tests further indicate that improvements are statistically reliable on high-capacity proprietary models, while results on smaller open-source models exhibit greater variance. Although these findings demonstrate the promise of adversarial refinement for CoT prompting, the conclusions remain preliminary. The effectiveness of adv-CoT depends on the base model’s reasoning capability, and the current evaluation is limited to four major categories of reasoning tasks. We will release the full implementation and prompts to support further investigation into broader applications and more generalizable prompt optimization strategies. Full article
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20 pages, 1598 KB  
Article
HGA-DP: Optimal Partitioning of Multimodal DNNs Enabling Real-Time Image Inference for AR-Assisted Communication Maintenance on Cloud-Edge-End Systems
by Cong Ye, Ruihang Zhang, Xiao Li, Wenlong Deng, Jianlei Wang and Sujie Shao
Information 2025, 16(12), 1091; https://doi.org/10.3390/info16121091 - 8 Dec 2025
Viewed by 352
Abstract
In the field of communication maintenance, Augmented Reality (AR) applications are critical for enhancing operational safety and efficiency. However, deploying the required multimodal models on resource-constrained terminal devices is challenging, as traditional cloud or on-device strategies fail to balance low latency and energy [...] Read more.
In the field of communication maintenance, Augmented Reality (AR) applications are critical for enhancing operational safety and efficiency. However, deploying the required multimodal models on resource-constrained terminal devices is challenging, as traditional cloud or on-device strategies fail to balance low latency and energy consumption. This paper proposes a Cloud-Edge-End collaborative inference framework tailored to multimodal model deployment. A subgraph partitioning strategy is introduced to systematically decompose complex multimodal models into functionally independent sub-units. Subsequently, a fine-grained performance estimation model is employed to accurately characterize both computation and communication costs across heterogeneous devices. And, a joint optimization problem is formulated to minimize end-to-end inference latency and terminal energy consumption. To solve this problem efficiently, a Hybrid Genetic Algorithm for DNN Partitioning (HGA-DP) evolved over 100 generations is designed, incorporating constraint-aware repair mechanisms and local neighborhood search to navigate the exponential search space of possible deployment combinations. Experimental results on a simulated three-tier collaborative computing platform demonstrate that, compared to traditional full on-device deployment, the proposed method reduces end-to-end inference latency by 70–80% and terminal energy consumption by 81.1%, achieving a 4.86× improvement in overall fitness score. Against the latency-optimized DADS heuristic, HGA-DP achieves 41.3% lower latency while reducing energy by 59.9%. Compared to the All-Cloud strategy, our approach delivers 71.5% latency reduction with only marginal additional terminal energy cost. This framework provides an adaptive and effective solution for real-time multimodal inference in resource-constrained scenarios, laying a foundation for intelligent, resource-aware deployment. Full article
(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)
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21 pages, 788 KB  
Article
Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content
by Yehudit Aperstein, Alon Gottlib, Gal Benita and Alexander Apartsin
Information 2025, 16(12), 1090; https://doi.org/10.3390/info16121090 - 8 Dec 2025
Viewed by 658
Abstract
Understanding semantic relations between two texts is crucial for many information and document management tasks, in which one must determine whether the content fully overlaps, is completely superseded by another document, or overlaps only partially, with unique information in each. Beyond establishing this [...] Read more.
Understanding semantic relations between two texts is crucial for many information and document management tasks, in which one must determine whether the content fully overlaps, is completely superseded by another document, or overlaps only partially, with unique information in each. Beyond establishing this relation, it is equally important to provide explainable outputs that specify which pieces of information are present, missing, or newly added between the text pair. In this study, we formally define semantic relations between two texts through the set-theoretic relation between their respective Answerable Question Sets (AQS), the sets of questions each text can answer. Under this formulation, Semantic Text Relation (STR), such as equivalence, inclusion, and mutual overlap, becomes a well-defined set relation between the corresponding texts’ AQSs. The set differences between the AQSs also serve as an explanation or diagnostic tool for identifying how the information in the texts diverges. Using this definition, we construct a synthetic benchmark that captures fine-grained informational relations through controlled paraphrasing and deliberate information removal supported by AQS manipulations. We then use this dataset to evaluate several discriminative and generative models for classifying text pairs into STR categories, assessing how well different model architectures capture semantic relations beyond surface-level similarity. We publicly release both the dataset and the data generation code to support further research. Full article
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11 pages, 216 KB  
Article
RNN-Based F0 Estimation Method with Attention Mechanism
by Ales Jandera, Martin Muzelak and Tomas Skovranek
Information 2025, 16(12), 1089; https://doi.org/10.3390/info16121089 - 7 Dec 2025
Viewed by 330
Abstract
Fundamental frequency estimation, also known as F0 estimation, is a crucial task in speech processing and analysis, with significant applications in areas such as speech recognition, speaker identification, and emotion detection. Traditional algorithms, while effective, often encounter challenges in real-time environments due to [...] Read more.
Fundamental frequency estimation, also known as F0 estimation, is a crucial task in speech processing and analysis, with significant applications in areas such as speech recognition, speaker identification, and emotion detection. Traditional algorithms, while effective, often encounter challenges in real-time environments due to computational limitations. Recent advances in deep learning, especially in the use of recurrent neural networks (RNNs), have opened new opportunities for enhancing F0 estimation accuracy and efficiency. This paper introduces a novel RNN-based F0 estimation method with an attention mechanism and evaluates its performance against selected state-of-the-art F0 estimation approaches, including standard baseline methods, as well as neural-network-based regression and classification models. By integrating attention mechanisms, the model eliminates the necessity for post-processing steps and enables a more efficient seq2scal estimation process. While the self-attention mechanism used in Transformers captures all pairwise temporal dependencies at a quadratic computational cost, the proposed method’s implementation of the attention mechanism enables it to selectively focus on the most relevant acoustic cues for F0 prediction, enhancing robustness without increasing the model’s complexity. Experimental results using the LibriSpeech and Common Voice datasets demonstrate superior computational efficiency of the proposed method compared to current state-of-the-art RNN-based seq2seq models, while maintaining comparable estimation accuracy. Furthermore, the proposed “RNN-based F0 estimation method with an attention mechanism” achieves the lowest computational complexity among all compared models, while maintaining high accuracy, making it suitable for low-latency, resource-limited deployments and competitive even with standard baseline methods, such as pYIN or CREPE. Finally, the performance of the developed RNN-based F0 estimation method with attention mechanism in terms of RMSE and FLOPs demonstrates the potential of attention mechanisms and sequence modelling in achieving high accuracy alongside lightweight F0 estimation suitable for modern speech processing applications, which aligns with the growing trend towards deploying intelligent systems on resource-constrained devices. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning, 2nd Edition)
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20 pages, 29695 KB  
Article
Interactive Visualisation of Complex Street Network Graphs from OSM in New Zealand
by Jun Yi Ng, Jing Ma, Anuradha Singh, Edmund M.-K. Lai and Steven Hayman
Information 2025, 16(12), 1088; https://doi.org/10.3390/info16121088 - 7 Dec 2025
Viewed by 292
Abstract
Street network graphs model interconnected land transport infrastructure, including roads and intersections, enabling traffic analysis, route planning, and network optimization. Directed network graphs (digraphs) add directionality to these connections, reflecting one-way streets and complex traffic flows. While OpenStreetMap (OSM) offers extensive data, visualizing [...] Read more.
Street network graphs model interconnected land transport infrastructure, including roads and intersections, enabling traffic analysis, route planning, and network optimization. Directed network graphs (digraphs) add directionality to these connections, reflecting one-way streets and complex traffic flows. While OpenStreetMap (OSM) offers extensive data, visualizing large-scale directed networks with complex junctions remains computationally challenging for browser-based tools. This paper presents an interactive visualization tool integrating OSM data with the New Zealand Transport Agency’s National Network Performance (NNP) analysis toolbox using PyDeck and WebGL. We introduce a directional offset algorithm to resolve edge overlaps and a geometry-aware node placement method for complex intersections. Experimental results demonstrate that our PyDeck implementation significantly outperforms existing solutions like Bokeh and OSMnx. On standard datasets, the system achieves up to 238× faster processing speeds and a 93% reduction in output file size compared to Bokeh. Furthermore, it successfully renders metropolitan-scale networks (∼1.3 million elements) where traditional visualisation tools fail to execute. This visualisation approach serves as a critical debugging instrument for NNP, allowing transport modellers to efficiently identify connectivity errors and validate the structural integrity of large-scale transport models. Full article
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25 pages, 8373 KB  
Article
Performance Improvement of Vehicle and Human Localization and Classification by YOLO Family Networks in Noisy UAV Images
by Viktor Makarichev, Rostyslav Tsekhmystro, Vladimir Lukin and Dmytro Krytskyi
Information 2025, 16(12), 1087; https://doi.org/10.3390/info16121087 - 7 Dec 2025
Viewed by 325
Abstract
Many important tasks in smart city development and management are solved by systems of monitoring and control installed on-board of unmanned aerial vehicles (UAVs). UAV sensors can be imperfect or they can operate in unfavorable conditions, which can then result in obtaining images [...] Read more.
Many important tasks in smart city development and management are solved by systems of monitoring and control installed on-board of unmanned aerial vehicles (UAVs). UAV sensors can be imperfect or they can operate in unfavorable conditions, which can then result in obtaining images or video sequences that are noisy. Noise can degrade the performance of methods of vehicle and human localization and classification. Therefore, specific techniques to improve performance have to be applied. In this paper, we consider YOLO family neural networks as tools for solving the aforementioned tasks. This family of networks is rapidly developing; however, the input data may still require pre-processing. One option is to apply denoising before object localization and classification. In addition, approaches based on augmentation and training can be used as well. We consider the performance of these approaches for various noise intensities. We identify the noise levels at which network performance starts to degrade and analyze possibilities of performance improvement for two filters–BM3D and DRUNet. Both improve such performance criteria as the F1 score, the Intersection over Union and the mean Average Precision. Datasets of urban areas are used in the network training and verification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Smart Cities)
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21 pages, 1290 KB  
Article
NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation
by Hongwei Zhang, Guolong Wang and Xiaofeng Yan
Information 2025, 16(12), 1086; https://doi.org/10.3390/info16121086 - 7 Dec 2025
Viewed by 243
Abstract
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs) have achieved significant success, they often overlook [...] Read more.
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs) have achieved significant success, they often overlook the diversity and dynamics of user preferences. To address these issues, researchers have begun to employ Hypergraph Convolutional Networks (HGCNs) for disentangled representation learning. However, two critical problems have received less attention: (1) the limited expressive capacity of conventional hypergraph convolution layers, which restricts the modeling of complex nonlinear user–POI preference interactions and consequently weakens generalization performance, and (2) the inadequate utilization of contrastive learning mechanisms, which prevents fully capturing cross-view collaborative signals and limits the exploitation of complementary multi-view information. To tackle these challenges, we propose a Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning (NE-DCHL) for next POI recommendation. The proposed model enhances nonlinear modeling capability and generalization by integrating ReLU activation, residual connections, and dropout regularization within the hypergraph convolution layer. A K-Nearest Neighbor (KNN)-based weighted adjacency matrix is employed to construct the geographical-view hypergraph, reducing computational complexity while maintaining essential spatial correlations. Moreover, a mini-batch InfoNCE loss and the GRACE (deep GRAph Contrastive rEpresentation learning) framework are utilized to improve efficiency and cross-view collaboration. Extensive experiments on two real-world datasets demonstrate that NE-DCHL consistently outperforms the original DCHL and other state-of-the-art approaches. Full article
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30 pages, 816 KB  
Article
Ternary LWE Key Search: A New Frontier for Quantum Combinatorial Attacks
by Yang Li
Information 2025, 16(12), 1085; https://doi.org/10.3390/info16121085 - 7 Dec 2025
Viewed by 253
Abstract
The Learning with Errors (LWE) problem, particularly its efficient ternary variant where secrets and errors are small, is a fundamental building block for numerous post-quantum cryptographic schemes. Combinatorial attacks provide a potent approach to cryptanalyzing ternary LWE. While classical attacks have achieved complexities [...] Read more.
The Learning with Errors (LWE) problem, particularly its efficient ternary variant where secrets and errors are small, is a fundamental building block for numerous post-quantum cryptographic schemes. Combinatorial attacks provide a potent approach to cryptanalyzing ternary LWE. While classical attacks have achieved complexities close to their asymptotic S0.25 bound for a search space of size S, their quantum counterparts have faced a significant gap: the attack by van Hoof et al. (vHKM) only reached a concrete complexity of S0.251, far from its asymptotic promise of S0.193. This work introduces an efficient quantum combinatorial attack that substantially narrows this gap. We present a quantum walk adaptation of the locality-sensitive hashing algorithm by Kirshanova and May, which fundamentally removes the need for guessing error coordinates—the primary source of inefficiency in the vHKM approach. This crucial improvement allows our attack to achieve a concrete complexity of approximately S0.225, markedly improving over prior quantum combinatorial methods. For concrete parameters of major schemes including NTRU, BLISS, and GLP, our method demonstrates substantial runtime improvements over the vHKM attack, achieving speedup factors ranging from 216 to 260 across different parameter sets and establishing the new state-of-the-art for quantum combinatorial attacks. As a second contribution, we address the challenge of polynomial quantum memory constraints. We develop a hybrid approach combining the Kirshanova–May framework with a quantum claw-finding technique, requiring only O(n) qubits while utilizing exponential classical memory. This work provides the first comprehensive concrete security analysis of real-world LWE-based schemes under such practical quantum resource constraints, offering crucial insights for post-quantum security assessments. Our results reveal a nuanced landscape where our combinatorial attacks are superior for small-weight parameters, while lattice-based attacks maintain an advantage for others. Full article
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