Information

13 pages, 1381 KiB

Open AccessSystematic Review

A Systematic Review of the Use and Effect of Virtual Reality, Augmented Reality and Mixed Reality in Physical Education

by Salvador Pérez-Muñoz, Raimundo Castaño Calle, Paula Teresa Morales Campo and Alberto Rodríguez-Cayetano

Information 2024, 15(9), 582; https://doi.org/10.3390/info15090582 - 21 Sep 2024

Abstract

New technologies are tools that are present in daily life on a regular basis. In order to improve the didactic process, education must take into account these new technologies. In the field of physical education, the significance of these technologies is reflected in [...] Read more.

New technologies are tools that are present in daily life on a regular basis. In order to improve the didactic process, education must take into account these new technologies. In the field of physical education, the significance of these technologies is reflected in the existence of applications that can be carried out within the field, both for educational purposes and for physical fitness and health. This is due to the potential presented by virtual reality, augmented reality and mixed reality. The objective of this study was to examine the utilisation and impact of AR, VR and MR technologies in physical education at the compulsory stage. In order to achieve this objective, a design based on the PRISMA methodology for conducting systematic reviews was employed. The databases of WOS, Scopus, PubMed and Google Scholar were subjected to analysis. The results indicate that there has been a notable increase in research activity in this field in recent years. The analysis yielded four principal areas of focus, namely the utilisation of pedagogical methodologies, the enhancement of motor and health-related competencies, and moreover, the facilitation of optimal integration of students in physical education. The utilisation and consequences of novel technologies represent a suitable instrument for enhancing the educational experience of students enrolled in physical education programmes. Full article

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

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18 pages, 1657 KiB

Open AccessArticle

Identification of Emerging Technological Hotspots from a Multi-Source Information Perspective: Case Study on Blockchain Financial Technology

by Ruiyu Hu, Zemenghong Bao, Juncheng Jia and Kun Lv

Information 2024, 15(9), 581; https://doi.org/10.3390/info15090581 - 19 Sep 2024

Abstract

In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to [...] Read more.

In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to accurately grasp the developmental trajectory of these tools and for their efficient utilization. In this study, we chronologically categorize information derived from five types of multi-source data, including journal articles, patent inventions, and industry reports, into distinct periods. We employ the LDA (Latent Dirichlet Allocation) topic model to identify emerging technological themes within these periods and utilize a dual-index theme lifecycle analysis method to construct a hotspot theme distribution map, thereby facilitating the extraction of significant themes. Through empirical research on blockchain financial technology, we ultimately identify 22 thematic areas of blockchain finance and extracted eight prominent themes, including financial technology, cross-border payments, digital invoices, supply chain finance, and decentralization. By analyzing these themes alongside their respective popularity levels, we validate that the methods above can be used to effectively identify emerging technological hotspots and illuminate their developmental directions. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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35 pages, 836 KiB

Open AccessArticle

Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation

by Ellie S. Paek, Talyn Fan, James D. Finch and Jinho D. Choi

Information 2024, 15(9), 580; https://doi.org/10.3390/info15090580 - 19 Sep 2024

Abstract

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, [...] Read more.

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)

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18 pages, 1217 KiB

Open AccessReview

User Experience and Usability of Voice User Interfaces: A Systematic Literature Review

by Akshay Madhav Deshmukh and Ricardo Chalmeta

Information 2024, 15(9), 579; https://doi.org/10.3390/info15090579 - 19 Sep 2024

Abstract

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with [...] Read more.

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with VUIs. It proposes a classification framework comprising six research categories to systematically organize the existing literature, analyzes the primary research streams, and identifies future research directions within each category. This systematic literature review provides a comprehensive analysis of the development and effectiveness of VUIs in facilitating natural human–machine interaction. It offers critical insights into the user experience of VUIs, contributing to the refinement of VUI design to optimize overall user interaction and satisfaction. Full article

(This article belongs to the Special Issue Intelligent Information Technology)

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19 pages, 2296 KiB

Open AccessArticle

A Hybrid Approach to Ontology Construction for the Badini Kurdish Language

by Media Azzat, Karwan Jacksi and Ismael Ali

Information 2024, 15(9), 578; https://doi.org/10.3390/info15090578 - 19 Sep 2024

Abstract

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some [...] Read more.

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. Full article

(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)

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23 pages, 1584 KiB

Open AccessArticle

Real-Time Identification and Nonlinear Control of a Permanent-Magnet Synchronous Motor Based on a Physics-Informed Neural Network and Exact Feedback Linearization

by Sergio Velarde-Gomez and Eduardo Giraldo

Information 2024, 15(9), 577; https://doi.org/10.3390/info15090577 - 19 Sep 2024

Abstract

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the [...] Read more.

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

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16 pages, 1486 KiB

Open AccessArticle

Challenges and Trends in Student Evaluation of Teaching: Analysis of SET Data from the University of Peloponnese

by Ilias Papadogiannis, Costas Vassilakis, Manolis Wallace and Athanassios Katsis

Information 2024, 15(9), 576; https://doi.org/10.3390/info15090576 - 19 Sep 2024

Abstract

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates [...] Read more.

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates were notably low, averaging 14.63%, with postgraduate students showing higher rates (27.33%) than undergraduates (10.77%). The average SET scores were moderately high, with postgraduates rating their courses slightly better (M = 4.137) than undergraduates (M = 3.899). A weak positive correlation was found between course grades and evaluations among undergraduates, whereas no significant correlation was observed for postgraduates. These findings highlight challenges in using SETs as reliable measures of teaching effectiveness and suggest the need for improved participation and more comprehensive evaluation methods. The results provide insights into enhancing assessment practices and contribute to the broader discourse on the validity of student evaluations in higher education. Full article

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17 pages, 4471 KiB

Open AccessArticle

Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

by Hai Lv, Yangyang Liu, Huimin Yin, Jingzhi Xi and Pingmin Wei

Information 2024, 15(9), 575; https://doi.org/10.3390/info15090575 - 18 Sep 2024

Abstract

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed [...] Read more.

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed 2422 original articles published between 2020 and 2023 with bibliometric tools such as Histcite Pro 2.1, Bibliometrix, CiteSpace, and VOSviewer. The United States, China, and India emerged as the most prolific countries, with Stanford University producing the most publications and Huazhong University of Science and Technology receiving the most citations. The National Natural Science Foundation of China and the National Institutes of Health have made significant contributions to this field. Scientific Reports is the most frequent journal for publishing these articles. Current research focuses on deep learning, federated learning, image classification, air pollution, mental health, sentiment analysis, and drug repurposing. In conclusion, this study provides detailed insights into the key authors, countries, institutions, funding agencies, and journals in the field, as well as the most frequently used keywords. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

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25 pages, 4208 KiB

Open AccessArticle

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva and Pedro Martins

Information 2024, 15(9), 574; https://doi.org/10.3390/info15090574 - 18 Sep 2024

Abstract

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the [...] Read more.

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. Full article

(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)

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21 pages, 1323 KiB

Open AccessArticle

Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden Ring

by Fatemeh Dehghani and Loutfouz Zaman

Information 2024, 15(9), 573; https://doi.org/10.3390/info15090573 - 18 Sep 2024

Abstract

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers [...] Read more.

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models’ outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful topics. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)

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26 pages, 436 KiB

Open AccessArticle

May the Source Be with You: On ChatGPT, Cybersecurity, and Secure Coding

by Tiago Espinha Gasiba, Andrei-Cristian Iosif, Ibrahim Kessba, Sathwik Amburi, Ulrike Lechner and Maria Pinto-Albuquerque

Information 2024, 15(9), 572; https://doi.org/10.3390/info15090572 - 18 Sep 2024

Abstract

Software security is an important topic that is gaining more and more attention due to the rising number of publicly known cybersecurity incidents. Previous research has shown that one way to address software security is by means of a serious game, the CyberSecurity [...] Read more.

Software security is an important topic that is gaining more and more attention due to the rising number of publicly known cybersecurity incidents. Previous research has shown that one way to address software security is by means of a serious game, the CyberSecurity Challenges, which are designed to raise awareness of software developers of secure coding guidelines. This game, proven to be very successful in the industry, makes use of an artificial intelligence technique (laddering technique) to implement a chatbot for human–machine interaction. Recent advances in machine learning have led to a breakthrough, with the implementation and release of large language models, now freely available to the public. Such models are trained on a large amount of data and are capable of analyzing and interpreting not only natural language but also source code in different programming languages. With the advent of ChatGPT, and previous state-of-the-art research in secure software development, a natural question arises: to what extent can ChatGPT aid software developers in writing secure software? In this work, we draw on our experience in the industry, and also on extensive previous work to analyze and reflect on how to use ChatGPT to aid secure software development. Towards this, we conduct two experiments with large language models. Our engagements with ChatGPT and our experience in the field allow us to draw conclusions on the advantages, disadvantages, and limitations of the usage of this new technology. Full article

(This article belongs to the Special Issue Next-Generation Programming Education: Integrating Generative AI and Collaborative Tools for Cutting-Edge Learning Experiences)

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24 pages, 3952 KiB

Open AccessArticle

Confrontation of Capitalism and Socialism in Wikipedia Networks

by Leonardo Ermann and Dima L. Shepelyansky

Information 2024, 15(9), 571; https://doi.org/10.3390/info15090571 - 18 Sep 2024

Abstract

We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the [...] Read more.

We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration was the opinion confrontation between capitalism, imperialism (blue opinion) and socialism, communism (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We found that the global network opinion favors socialism, communism for all six editions. The model also determined the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between Christianity and Islam, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks. Full article

(This article belongs to the Special Issue Network and Hypernetwork Science: Emerging Models, Paradigms, and Applications)

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19 pages, 7882 KiB

Open AccessArticle

A Modular XR Collaborative Platform for Occupational Safety and Health Training: A Case Study in Circular Logistics Facilities

by Ali Vatankhah Barenji, Jorge E. Garcia and Benoit Montreuil

Information 2024, 15(9), 570; https://doi.org/10.3390/info15090570 - 18 Sep 2024

Abstract

Over the past few years, safety and health have become major concerns in the warehouse and logistics sectors. Each year, warehouse fatalities, injuries, and accidents cause unrecoverable losses and huge financial costs. In spite of all the advancements in methods, tools, equipment, and [...] Read more.

Over the past few years, safety and health have become major concerns in the warehouse and logistics sectors. Each year, warehouse fatalities, injuries, and accidents cause unrecoverable losses and huge financial costs. In spite of all the advancements in methods, tools, equipment, and regulations, the number of accidents, especially fatal ones, has not subsided significantly. As a result, safety professionals and researchers have explored new and innovative ways to combat this problem. In the circular logistics facility (CLF) industry, located inside warehouses and providing human muscle-oriented services to maintain pallets, both short-term safety incidents and long-term health concerns are present. Long-term health training is rarely discussed in the literature compared to short-term safety training. This is because health issues are more complex than safety issues, since biological outcomes may take time to develop, are affected by multiple resources, and cumulative injuries may occur. This paper contributes to warehouse health and safety by designing and developing a modular XR collaborative training and testing platform (MXC-P). The co-design process is applied to design each module in the MXC-P. Three main modules related to health and safety training for CLF were considered, namely personal protection equipment, pallet handling, and pallet repairing. On this platform, a virtual interactive world provides a solid hands-on training environment and generates syntactic data for evaluating long-term health risks. On the other hand, collaborative and modular environments provide a solution to geographically distributed systems, allowing employees to connect and train remotely. The effectiveness of the MXC-P is compared with traditional safety training in a pilot study. Based on the results, we can establish that the MXC-P is effective in teaching and testing hazard identification situations, especially those relating to short-term health. The results also indicate that trainees’ recall of knowledge would improve with the MXC-P. In addition to this, the MXC-P can also be used to test and evaluate a new system and generate syntactic data for evaluating long-term health. Full article

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

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15 pages, 460 KiB

Open AccessArticle

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

by Antonella Di Stefano, Massimo Gollo and Giovanni Morana

Information 2024, 15(9), 569; https://doi.org/10.3390/info15090569 - 18 Sep 2024

Abstract

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists [...] Read more.

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm. Full article

(This article belongs to the Special Issue Fundamental Problems of Information Studies)

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21 pages, 1072 KiB

Open AccessArticle

Community Detection Using Deep Learning: Combining Variational Graph Autoencoders with Leiden and K-Truss Techniques

by Jyotika Hariom Patil, Petros Potikas, William B. Andreopoulos and Katerina Potika

Information 2024, 15(9), 568; https://doi.org/10.3390/info15090568 - 16 Sep 2024

Abstract

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information [...] Read more.

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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16 pages, 863 KiB

Open AccessArticle

The Enhancement of Statistical Literacy: A Cross-Institutional Study Using Data Analysis and Text Mining to Identify Statistical Issues in the Transition to University Education

by Antonio de la Hoz-Ruiz, Emma Howard and Raquel Hijón-Neira

Information 2024, 15(9), 567; https://doi.org/10.3390/info15090567 - 14 Sep 2024

Abstract

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) [...] Read more.

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) provides support for various mathematics-related subjects, with statistics students being the second-largest cohort of visitors. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support; the categorization of statistical concepts has been made with the statistics module description for each statistics subject at the Universidad Rey Juan Carlos (URJC). First, we conducted a categorization of statistical concepts taught in university (based on URJC’s catergorization); after that, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. The statistical issues presented were categorized as descriptive statistics (22.8%), probability (44%), statistical inference (29.2%), and statistical software (4%). Students struggled with material that was introduced at university level rather than material seen at secondary level. Our findings on students’ main statistical issues contribute to the development of a suite of evidence-based educational applications and games to support undergraduate students internationally in first- and second-year statistical modules. Full article

(This article belongs to the Special Issue Advanced Computer and Digital Technologies)

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15 pages, 930 KiB

Open AccessArticle

The Effect of Augmented Reality on Learning Meiosis via Guided Inquiry and Pecha Kucha: A Quasi-Experimental Design

by António Faria and Guilhermina Lobato Miranda

Information 2024, 15(9), 566; https://doi.org/10.3390/info15090566 - 13 Sep 2024

Abstract

This study investigates the effectiveness of using augmented reality (AR), combined with guided inquiry and the Pecha Kucha technique, on students’ academic outcomes when learning meiosis. The main objective was to analyse whether this combination presents significant differences in the academic performance of [...] Read more.

This study investigates the effectiveness of using augmented reality (AR), combined with guided inquiry and the Pecha Kucha technique, on students’ academic outcomes when learning meiosis. The main objective was to analyse whether this combination presents significant differences in the academic performance of students in the experimental group (EG) compared to the control group (CG), who did not use AR. The research employed a quasi-experimental design involving three 11th-grade classes from a secondary school in Lisbon. Knowledge tests were administered post-intervention and at follow-up to assess the impact. To ensure the normality of the distributions, a Shapiro–Wilk test was applied and, to guarantee the homogeneity of variances, a Levene test was utilised. Independent and paired sample t-tests were performed. The results indicated that the innovative approach, combining AR with guided inquiry and Pecha Kucha, enhanced student engagement and led to improved academic performance. The study highlights the importance of teacher support during guided inquiry, showing that proper guidance maximises learning outcomes. Findings suggest that integrating active methodologies and current technologies can enrich Biology teaching and improve understanding of complex concepts like meiosis. This research contributes to existing literature by demonstrating the potential of AR, guided inquiry, and the Pecha Kucha technique in enhancing educational outcomes. Full article

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24 pages, 4447 KiB

Open AccessArticle

LPG Semantic Ontologies: A Tool for Interoperable Schema Creation and Management

by Eleonora Bernasconi, Miguel Ceriani and Stefano Ferilli

Information 2024, 15(9), 565; https://doi.org/10.3390/info15090565 - 13 Sep 2024

Abstract

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data [...] Read more.

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder’s architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder’s strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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23 pages, 1172 KiB

Open AccessArticle

Prevention of Cardiometabolic Syndrome in Children and Adolescents Using Machine Learning and Noninvasive Factors: The CASPIAN-V Study

by Hamid Reza Marateb, Mahsa Mansourian, Amirhossein Koochekian, Mehdi Shirzadi, Shadi Zamani, Marjan Mansourian, Miquel Angel Mañanas and Roya Kelishadi

Information 2024, 15(9), 564; https://doi.org/10.3390/info15090564 - 13 Sep 2024

Abstract

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with [...] Read more.

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% ± 4.8) and specificity (78.8% ± 13.7), with an area under the ROC curve (AUC) of 0.867 ± 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p < 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents. Full article

(This article belongs to the Section Artificial Intelligence)

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