Information

28 pages, 5017 KiB

Open AccessArticle

A Cybersecurity Risk Assessment for Enhanced Security in Virtual Reality

by Rebecca Acheampong, Dorin-Mircea Popovici, Titus C. Balan, Alexandre Rekeraho and Ionut-Alexandru Oprea

Information 2025, 16(6), 430; https://doi.org/10.3390/info16060430 (registering DOI) - 23 May 2025

Abstract

Our society is becoming increasingly dependent on technology, with immersive virtual worlds such as Extended Reality (XR) transforming how we connect and interact. XR technologies enhance communication and operational efficiency. They have been adopted in sectors such as manufacturing, education, and healthcare. However, [...] Read more.

Our society is becoming increasingly dependent on technology, with immersive virtual worlds such as Extended Reality (XR) transforming how we connect and interact. XR technologies enhance communication and operational efficiency. They have been adopted in sectors such as manufacturing, education, and healthcare. However, the immersive and interconnected nature of XR introduces security risks that span from technical and human to psychological vulnerabilities. In this study, we examined security threats in XR environments through a scenario-driven risk assessment, using a hybrid approach combining Common Vulnerability Scoring System (CVSS) metrics and a custom likelihood model to quantify risks. This methodology provides a comprehensive risk evaluation method, identifying critical vulnerabilities such as Remote Code Execution (RCE), social engineering, excessive permission exploitation, unauthorized access, and data exfiltration. The findings reveal that human vulnerabilities, including users’ susceptibility to deception and excessive trust in familiar interfaces and system prompts, significantly increase attack success rates. Additionally, developer mode, once enabled, remains continuously active, and the lack of authentication requirements for installing applications from unknown sources, coupled with poor permission management on the part of the users, creates security gaps that attackers can exploit. Furthermore, permission management in XR devices is often broad and persistent and lacks real-time notifications, allowing malicious applications to exploit microphone, camera, and location access without the users knowing. By leveraging CVSS scores and a structured likelihood-based risk assessment, we quantified the severity of these threats, with RCE, social engineering, and insecure app installation emerging as the greatest risks. This study highlights the necessity of implementing granular permission controls, formalized developer mode restrictions, and structured user education programs to mitigate XR-specific threats. Full article

(This article belongs to the Special Issue Extended Reality and Cybersecurity)

27 pages, 991 KiB

Open AccessArticle

Integrating Bayesian Knowledge Tracing and Human Plausible Reasoning in an Adaptive Augmented Reality System for Spatial Skill Development

by Christos Papakostas, Christos Troussas, Akrivi Krouska and Cleo Sgouropoulou

Information 2025, 16(6), 429; https://doi.org/10.3390/info16060429 - 23 May 2025

Abstract

The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reasoning (HPR) [...] Read more.

The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reasoning (HPR) to design an AR system that can adapt to dynamic simulations with quantitative as well as qualitative cognitive methodologies. The system records a broad range of interactions from users, such as objects being rotated, changes in viewing perspective, and time spent on tasks, which are later analyzed through probabilistic updates with respect to skill building along with rule-based reasoning for determining behavioral patterns. Results from an in-depth case study show that the BKT module properly tracks improvement in spatial skills, while the HPR application highlights suboptimal approaches that hide underlying conceptual understanding. The adaptive system used then provides metacognitive hints that adjust by optimizing task difficulty levels, leading to improved student performance compared to standard non-adaptive AR techniques. Results show that using BKT and HPR in an AR environment not only enables accurate task performance but supports greater insight in approach strategies, leading to better and transferable spatial skills. Full article

(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)

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27 pages, 759 KiB

Open AccessArticle

Mitigating Impact of Data Poisoning Attacks on CPS Anomaly Detection with Provable Guarantees

by Sahar Abedzadeh and Shameek Bhattacharjee

Information 2025, 16(6), 428; https://doi.org/10.3390/info16060428 - 23 May 2025

Abstract

Anomaly-based attack detection methods depend on some form of machine learning to detect data falsification attacks in smart living cyber–physical systems. However, there is a lack of studies that consider the presence of attacks during the training phase and their effect on detection [...] Read more.

Anomaly-based attack detection methods depend on some form of machine learning to detect data falsification attacks in smart living cyber–physical systems. However, there is a lack of studies that consider the presence of attacks during the training phase and their effect on detection and false alarm performance. To improve the robustness of time series learning for anomaly detection, we propose a framework by modifying design choices such as regression error type and loss function type while learning the thresholds for an anomaly detection framework during the training phase. Specifically, we offer theoretical proofs on the relationship between poisoning attack strengths and how that informs the choice of loss functions used to learn the detection thresholds. This, in turn, leads to explainability of why and when our framework mitigates data poisoning and the trade-offs associated with such design changes. The theoretical results are backed by experimental results that prove attack mitigation performance with NIST-specified metrics for CPS, using real data collected from a smart metering infrastructure as a proof of concept. Thus, the contribution is a framework that guarantees security of ML and ML for security simultaneously. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

31 pages, 1706 KiB

Open AccessArticle

Enhancing EFL Speaking Skills with AI-Powered Word Guessing: A Comparison of Human and AI Partners

by Mondheera Pituxcoosuvarn, Midori Tanimura, Yohei Murakami and Jeremy Stewart White

Information 2025, 16(6), 427; https://doi.org/10.3390/info16060427 - 23 May 2025

Abstract

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI [...] Read more.

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retelling task indicated modest improvements in fluency and complexity, particularly following interaction with the AI partner. Accuracy scores remained largely stable across conditions. Post-task reflections revealed that learners perceived AI partners as less intimidating, facilitating more relaxed language production, though concerns were noted regarding limited responsiveness. Qualitative analysis of the gameplay transcripts further revealed contrasting interactional patterns: AI partners elicited more structured interactions whereas human partners prompted more spontaneous and variable interactions. These findings suggest that AI-mediated gameplay can enhance specific dimensions of spoken language development and may serve as a complementary resource alongside human interaction. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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15 pages, 347 KiB

Open AccessArticle

No Peace After Death? The Impact of AI-Driven Memorial Chatbots on Privacy and Data Protection

by Jacopo Ciani Sciolla and Ugo Pagallo

Information 2025, 16(6), 426; https://doi.org/10.3390/info16060426 - 22 May 2025

Abstract

This paper examines the profitable digital afterlife industry (DAI), whose aim is to monetize the digital remains of departed internet users. Among the different services offered by such an industry, attention is drawn to AI and humanoid robots that create convincing digital surrogates [...] Read more.

This paper examines the profitable digital afterlife industry (DAI), whose aim is to monetize the digital remains of departed internet users. Among the different services offered by such an industry, attention is drawn to AI and humanoid robots that create convincing digital surrogates of the deceased. A decade ago, Google patented robots that can be customizable with personality attributes, and later, Microsoft secured a patent for software that could reincarnate people as a chatbot. By focusing on how these technologies actually work, and in particular, on how software systems collect and process the deceased’s data, the intent is to illustrate the normative challenges of the field, namely, the legal puzzles, moral threats, and uncertainties related to privacy and data protection in the after-death governance of cyberspace. Full article

(This article belongs to the Special Issue Do (AI) Chatbots Pose any Special Challenges for Trust and Privacy?)

27 pages, 2692 KiB

Open AccessArticle

Developing an Expert System for Hardware Selection in Internet of Things-Based System Design: Grey ITARA-COBRA Approach (With an Example in the Agricultural Domain)

by Taha Ahmadi Pargo and Sarfaraz Hashemkhani Zolfani

Information 2025, 16(6), 425; https://doi.org/10.3390/info16060425 - 22 May 2025

Abstract

Internet of Things (IoT) technology is rapidly transforming various industries. Advancements in production technologies have made it more affordable to produce suitable hardware to create IoT-based systems. This has resulted in a wide range of options available at reasonable prices. Having multiple options [...] Read more.

Internet of Things (IoT) technology is rapidly transforming various industries. Advancements in production technologies have made it more affordable to produce suitable hardware to create IoT-based systems. This has resulted in a wide range of options available at reasonable prices. Having multiple options available gives designers creative freedom. However, having more options may confuse designers and make it more difficult to choose hardware that meets design needs. This paper presents an expert system for IoT-based system hardware selection. In the proposed approach, the hardware information and expert knowledge are stored in a database and a knowledge base. Users input their required specifications using a user interface, and then the system’s decision-making module constructs the decision matrix and eventually ranks the alternatives utilizing a hybrid ITARA-COBRA method. Due to the ambiguity in the data, grey numbers are used for decision-making. In the next step, an example of an agricultural IoT-based system design is applied to test the system. The proposed Grey ITARA method is compared with the Grey MEREC and Grey CRITIC methods, and given the use of the indifference threshold concept, it performs well. Moreover, its ability to handle unstructured, vague data is useful for using technical specifications and expert opinions in decision-making. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis, 3rd Edition)

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26 pages, 2187 KiB

Open AccessArticle

Enhancing Text Classification Through Grammar-Based Feature Engineering and Learning Models

by Alaa Mohasseb, Andreas Kanavos and Eslam Amer

Information 2025, 16(6), 424; https://doi.org/10.3390/info16060424 - 22 May 2025

Abstract

Text classification remains a challenging task in natural language processing (NLP) due to linguistic complexity and data imbalance. This study proposes a hybrid approach that integrates grammar-based feature engineering with deep learning and transformer models to enhance classification performance. A dataset of factoid [...] Read more.

Text classification remains a challenging task in natural language processing (NLP) due to linguistic complexity and data imbalance. This study proposes a hybrid approach that integrates grammar-based feature engineering with deep learning and transformer models to enhance classification performance. A dataset of factoid and non-factoid questions, further categorized into causal, choice, confirmation, hypothetical, and list types, is used to evaluate several models, including CNNs, BiLSTMs, MLPs, BERT, DistilBERT, Electra, and GPT-2. Grammatical and domain-specific features are explicitly extracted and leveraged to improve multi-class classification. To address class imbalance, the SMOTE algorithm is applied, significantly boosting the recall and F1-score for minority classes. Experimental results show that DistilBERT achieves the highest binary classification accuracy, equal to 94%, while BiLSTM and CNN outperform transformers in multi-class settings, reaching up to 92% accuracy. These findings confirm that grammar-based features provide critical syntactic and semantic insights, enhancing model robustness and interpretability beyond conventional embeddings. Full article

(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

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27 pages, 4244 KiB

Open AccessArticle

Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data

by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic and Lazar Spasovic

Information 2025, 16(6), 423; https://doi.org/10.3390/info16060423 - 22 May 2025

Abstract

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified [...] Read more.

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

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19 pages, 1649 KiB

Open AccessArticle

HS-SocialRec: A Study on Boosting Social Recommendations with Hard Negative Sampling in LightGCN

by Ziping Sheng and Lai Wei

Information 2025, 16(5), 422; https://doi.org/10.3390/info16050422 - 21 May 2025

Abstract

Most current graph neural network (GNN)-based social recommendation systems mainly extract negative samples from explicit feedback, and are unable to accurately learn the boundaries of similar positive and negative samples, which leads to misjudgment of user preferences. For this reason, we propose to [...] Read more.

Most current graph neural network (GNN)-based social recommendation systems mainly extract negative samples from explicit feedback, and are unable to accurately learn the boundaries of similar positive and negative samples, which leads to misjudgment of user preferences. For this reason, we propose to introduce the hop-mixing technique to synthesize hard negative samples for users to fully explore their preferences. Firstly, positive sample information is injected into the original negative samples in each layer to generate augmented negative samples that are very similar to the positive samples. Then the super-enhanced negative samples with the highest inner product score with the positive samples are identified from each layer, and finally, the super-enhanced negative samples from each layer are aggregated and pooled to obtain the final hard negative samples. Subsequently, a graph fusion mechanism is used to aggregate user representations from the social graph and the user–item bipartite graph. Comparative experiments on two real datasets and ten baseline models are conducted, and the results show that the proposed method has certain performance advantages over other state-of-the-art recommendation models. Full article

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22 pages, 2695 KiB

Open AccessArticle

Comparing Classification Algorithms to Recognize Selected Gestures Based on Microsoft Azure Kinect Joint Data

by Marc Funken and Thomas Hanne

Information 2025, 16(5), 421; https://doi.org/10.3390/info16050421 - 21 May 2025

Abstract

This study aims to explore the potential of exergaming (which can be used along with prescriptive medication for children with spinal muscular atrophy) and examine its effects on monitoring and diagnosis. The present study focuses on comparing models trained on joint data for [...] Read more.

This study aims to explore the potential of exergaming (which can be used along with prescriptive medication for children with spinal muscular atrophy) and examine its effects on monitoring and diagnosis. The present study focuses on comparing models trained on joint data for gesture detection, which has not been extensively explored in previous studies. The study investigates three approaches to detect gestures based on 3D Microsoft Azure Kinect joint data. We discuss simple decision rules based on angles and distances to label gestures. In addition, we explore supervised learning methods to increase the accuracy of gesture recognition in gamification. The compared models performed well on the recorded sample data, with the recurrent neural networks outperforming feedforward neural networks and decision trees on the captured motions. The findings suggest that gesture recognition based on joint data can be a valuable tool for monitoring and diagnosing children with spinal muscular atrophy. This study contributes to the growing body of research on the potential of virtual solutions in rehabilitation. The results also highlight the importance of using joint data for gesture recognition and provide insights into the most effective models for this task. The findings of this study can inform the development of more accurate and effective monitoring and diagnostic tools for children with spinal muscular atrophy. Full article

(This article belongs to the Special Issue Addressing Real-World Challenges in Recognition and Classification with Cutting-Edge AI Models and Methods)

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26 pages, 2252 KiB

Open AccessArticle

PROMPTHEUS: A Human-Centered Pipeline to Streamline Systematic Literature Reviews with Large Language Models

by Joao Torres, Catherine Mulligan, Joaquim Jorge and Catarina Moreira

Information 2025, 16(5), 420; https://doi.org/10.3390/info16050420 - 21 May 2025

Abstract

The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate systematic literature reviews (SLR), particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge [...] Read more.

The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate systematic literature reviews (SLR), particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge effectively, meaning academic literature is often misrepresented in the popular press and, more broadly, in society. Traditional SLRs are labor-intensive and error-prone, and they struggle to keep up with the rapid pace of new research. To address these issues, we developed PROMPTHEUS: an AI-driven pipeline solution that automates the systematic literature review (LR) process using large language models (LLMs). We aimed to enhance efficiency by reducing the manual workload while maintaining the precision and coherence required for comprehensive literature synthesis. PROMPTHEUS automates key SLR stages, including systematic searches, data extraction, and topic modeling using BERTopic and summarization with transformer models. Evaluations across five research domains demonstrated that PROMPTHEUS reduces review time, achieves high precision, and provides coherent topic organization, offering a scalable and effective solution for conducting literature reviews in an increasingly crowded research landscape. Full article

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22 pages, 609 KiB

Open AccessSystematic Review

Leveraging Learning Analytics to Improve the User Experience of Learning Management Systems in Higher Education Institutions

by Patrick Ngulube and Mthokozisi Masumbika Ncube

Information 2025, 16(5), 419; https://doi.org/10.3390/info16050419 - 20 May 2025

Abstract

This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and [...] Read more.

This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and delineates implementation challenges that contribute to data misinterpretation and underutilisation. Consequently, the absence of a systematic evaluation of analytical methodologies impedes the capacity of higher education institutes to tailor learning processes to individual student needs. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search was conducted across five academic databases. Studies employing learning analytics within Learning Management Systems environments to improve user experience in higher education institutions were included, while purely theoretical or non-higher education institution studies were excluded, resulting in a final corpus of 41 studies. Methodological rigour was assessed using the Critical Appraisal Skills Programme Checklist. This study revealed diverse learning analytics methodologies and a dual research focus on specific platforms and broader impacts on Learning Management Systems. However, ethical, implementation, generalisability, interpretation, personalisation, and system quality challenges impede effective learning analytics integration for user experience improvement, demanding rigorous and contextually aware strategies. This study’s reliance on existing literature introduces potential selection and database biases. As such, future research should prioritise empirical validation and cross-institutional studies to address these limitations. Full article

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19 pages, 641 KiB

Open AccessArticle

Big Five Personality Trait Prediction Based on User Comments

by Kit-May Shum, Michal Ptaszynski and Fumito Masui

Information 2025, 16(5), 418; https://doi.org/10.3390/info16050418 - 20 May 2025

Abstract

The study of personalities is a major component of human psychology, and with an understanding of personality traits, practical applications can be used in various domains, such as mental health care, predicting job performance, and optimising marketing strategies. This study explores the prediction [...] Read more.

The study of personalities is a major component of human psychology, and with an understanding of personality traits, practical applications can be used in various domains, such as mental health care, predicting job performance, and optimising marketing strategies. This study explores the prediction of Big Five personality trait scores from online comments using transformer-based language models, focusing on improving the model performance with a larger dataset and investigating the role of intercorrelations among traits. Using the PANDORA dataset from Reddit, the RoBERTa and BERT models, including both the base and large variants, were fine-tuned and evaluated to determine their effectiveness in personality trait prediction. Compared to previous work, our study utilises a significantly larger dataset to enhance the model’s generalisation and robustness. The results indicate that RoBERTa outperforms BERT across most metrics, with RoBERTa large achieving the best overall performance. In addition to evaluating the overall predictive accuracy, this study investigates the impact of intercorrelations among personality traits. A comparative analysis is conducted between a single-model approach, which predicts all five traits simultaneously, and a multiple-model approach, fine-tuning the models independently and each predicting a single trait. The findings reveal that the single-model approach achieves a lower RMSE and higher

R^{2}

values, highlighting the importance of incorporating trait intercorrelations in improving the prediction accuracy. Furthermore, RoBERTa large demonstrated a stronger ability to capture these intercorrelations compared to previous studies. These findings emphasise the potential of transformer-based models in personality computing and underscore the importance of leveraging both larger datasets and intercorrelations to enhance predictive performance. Full article

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21 pages, 406 KiB

Open AccessArticle

A Novel Method for Community Detection in Bipartite Networks

by Ali Khosrozadeh, Ali Movaghar, Mohammad Mehdi Gilanian Sadeghi and Hamidreza Mahyar

Information 2025, 16(5), 417; https://doi.org/10.3390/info16050417 - 20 May 2025

Abstract

The community structure is a major feature of bipartite networks, which serve as a typical model for empirical networks consisting of two kinds of nodes. Over the past years, community detection has drawn a lot of attention. Numerous methods for community detection have [...] Read more.

The community structure is a major feature of bipartite networks, which serve as a typical model for empirical networks consisting of two kinds of nodes. Over the past years, community detection has drawn a lot of attention. Numerous methods for community detection have been put forth. Nevertheless, some of them need a lot of time, which restricts their use in large networks. While several low-time complexity algorithms exist, their practical value in real-world applications is limited since they are typically non-deterministic. Typically, in bipartite networks, a unipartite projection of one part of the network is created, and then communities are detected inside that projection using methods for unipartite networks. Unipartite projections may yield incorrect or erroneous findings as they inevitably include a loss of information. In this paper, BiVoting, a two-mode and deterministic community detection method in bipartite networks, is proposed. This method is a consequence of bipartite modularity, which quantifies the strength of partitions and is based on how people vote in social elections. The proposed method’s performance was evaluated, and comparison with four common community detection methods in bipartite networks shows that for calculating the modularity score in large networks, BiVoting performs better than the best method. Full article

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29 pages, 3690 KiB

Open AccessArticle

Application of the Adaptive Mixed-Order Cubature Particle Filter Algorithm Based on Matrix Lie Group Representation for the Initial Alignment of SINS

by Ning Wang and Fanming Liu

Information 2025, 16(5), 416; https://doi.org/10.3390/info16050416 - 20 May 2025

Abstract

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which [...] Read more.

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which limit their applicability in high-precision navigation. To address these limitations, this paper proposes an adaptive mixed-order spherical simplex-radial cubature particle filter (MLG-AMSSRCPF) algorithm based on matrix Lie group representation. In this approach, attitude errors are represented on the matrix Lie group SO(3), while velocity errors and inertial sensor biases are retained in Euclidean space. Efficient bidirectional conversion between Euclidean and manifold spaces is achieved through exponential and logarithmic maps, enabling accurate attitude estimation without the need for Jacobian matrices. A hybrid-order cubature transformation is introduced to reduce model linearization errors, thereby enhancing the estimation accuracy. To improve the algorithm’s adaptability in dynamic noise environments, an adaptive noise covariance update mechanism is integrated. Meanwhile, the particle similarity is evaluated using Euclidean distance, allowing the dynamic adjustment of particle numbers to balance the filtering accuracy and computational load. Furthermore, a multivariate Huber loss function is employed to adaptively adjust particle weights, effectively suppressing the influence of outliers and significantly improving the robustness of the filter. Simulation and the experimental results validate the superior performance of the proposed algorithm under moving-base alignment conditions. Compared with the conventional cubature particle filter (CPF), the heading accuracy of the MLG-AMSSRCPF algorithm was improved by 31.29% under measurement outlier interference and by 39.79% under system noise mutation scenarios. In comparison with the unscented Kalman filter (UKF), it yields improvements of 58.51% and 58.82%, respectively. These results demonstrate that the proposed method substantially enhances the filtering accuracy, robustness, and computational efficiency of SINS, confirming its practical value for initial alignment in high-noise, complex dynamic, and nonlinear navigation systems. Full article

(This article belongs to the Section Artificial Intelligence)

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22 pages, 2114 KiB

Open AccessReview

Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications

by Thang Le Dinh, Manh-Chiên Vu and Giang T.C. Tran

Information 2025, 16(5), 415; https://doi.org/10.3390/info16050415 - 18 May 2025

Abstract

Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their [...] Read more.

Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their broad range of applications across business functions, and the strategies required for successful implementation. Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. The findings emphasize the importance of workforce training, robust technological infrastructure, data-driven cultures, and strategic partnerships for SMEs. Furthermore, the review highlights methods for measuring and optimizing AI’s value, such as tracking key performance indicators and improving customer satisfaction. While acknowledging challenges like financial constraints and ethical considerations, this research provides practical guidance for SMEs to effectively leverage AI for sustainable growth and provides a foundation for future studies to explore customized AI strategies for diverse SME contexts. Full article

(This article belongs to the Special Issue Emerging Applications of Machine Learning in Healthcare, Industry, and Beyond)

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34 pages, 7452 KiB

Open AccessSystematic Review

Knowledge Management Strategies Supported by ICT for the Improvement of Teaching Practice: A Systematic Review

by Miguel-Angel Romero-Ochoa, Julio-Alejandro Romero-González, Alonso Perez-Soltero, Juan Terven, Teresa García-Ramírez, Diana-Margarita Córdova-Esparza and Francisco-Alan Espinoza-Zallas

Information 2025, 16(5), 414; https://doi.org/10.3390/info16050414 - 18 May 2025

Abstract

In the modern digital ecosystem, the effective management of knowledge and the integration of information and communication technologies are the keys to revolutionizing educational practices within higher education institutions. This study presents a systematic review of recent literature, examining how the incorporation of [...] Read more.

In the modern digital ecosystem, the effective management of knowledge and the integration of information and communication technologies are the keys to revolutionizing educational practices within higher education institutions. This study presents a systematic review of recent literature, examining how the incorporation of information and communication technologies facilitates the creation and transfer of knowledge, enables collaboration among educators, and supports continuous professional development. We explore the benefits of personalized learning and the application of technological tools to enhance collaboration, access to educational resources, and pedagogical reflection. The key findings emphasize the role of these tools in promoting teacher interaction and exchange of ideas, highlighting the critical importance of training in digital competency to maximize their impact. The study also identifies challenges, including the need to improve effective knowledge transfer and technological training. In conclusion, effective knowledge management, supported by information and communication technologies, fortifies digital competencies and cultivates a culture of collaboration and content creation in higher education institutions. Full article

(This article belongs to the Special Issue Emerging Research in Knowledge Management and Innovation)

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13 pages, 1968 KiB

Open AccessArticle

Drunk Driver Detection Using Thermal Facial Images

by Chin-Heng Chai, Siti Fatimah Abdul Razak, Sumendra Yogarayan and Ramesh Shanmugam

Information 2025, 16(5), 413; https://doi.org/10.3390/info16050413 - 18 May 2025

Abstract

This study aims to investigate and propose a machine learning approach that can accurately detect alcohol consumption by analyzing the thermal patterns of facial features. Thermal images from the Tufts Face Database and self-collected images were utilized to train the models in identifying [...] Read more.

This study aims to investigate and propose a machine learning approach that can accurately detect alcohol consumption by analyzing the thermal patterns of facial features. Thermal images from the Tufts Face Database and self-collected images were utilized to train the models in identifying temperature variations in specific facial regions. Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) algorithms were employed to extract facial features, while classifiers such as Support Vector Machines (SVMs), Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNN), as well as Random Forest and linear regression, classify individuals as sober or intoxicated based on their thermal images. The models’ effectiveness in analyzing thermal images to determine alcohol intoxication is expected to provide a foundation for the development of a realistic drunk driver detection system based on thermal images. In this study, MLP obtained 90% accuracy and outperformed the other models in classifying the thermal images, either as sober or showing signs of alcohol consumption. The trained models may be embedded in advanced drunk detection systems as part of an in-vehicle safety application. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

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21 pages, 1780 KiB

Open AccessArticle

Information Model for Pharmaceutical Smart Factory Equipment Design

by Roland Wölfle, Irina Saur-Amaral and Leonor Teixeira

Information 2025, 16(5), 412; https://doi.org/10.3390/info16050412 - 17 May 2025

Abstract

Pharmaceutical production typically focuses on individual drug types for each production line, which limits flexibility. However, the emergence of Industry 4.0 technologies presents new opportunities for more adaptable and customized manufacturing processes. Despite this promise, the development of innovative design techniques for pharmaceutical [...] Read more.

Pharmaceutical production typically focuses on individual drug types for each production line, which limits flexibility. However, the emergence of Industry 4.0 technologies presents new opportunities for more adaptable and customized manufacturing processes. Despite this promise, the development of innovative design techniques for pharmaceutical production equipment remains incomplete. Manufacturers encounter challenges due to rapid innovation cycles while adhering to stringent Good Manufacturing Practice (GMP) standards. Our research addresses this issue by introducing an information model that organizes the design, development, and testing of pharmaceutical manufacturing equipment. This model is based on an exploratory review of 176 articles concerning design principles in regulated industries and integrates concepts from Axiomatic Design, Quality by Design, Model-Based Systems Engineering, and the V-Model framework. Further refinement was achieved through insights from 10 industry experts. The resultant workflow-based information model can be implemented as software to enhance engineering and project management. This research offers a structured framework that enables pharmaceutical equipment manufacturers and users to collaboratively develop solutions in an iterative manner, effectively closing the gap between industry needs and systematic design methodologies. Full article

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