Information

12 pages, 5319 KiB

Open AccessArticle

A Method for Maintaining a Unique Kurume Kasuri Pattern of Woven Textile Classified by EfficientNet by Means of LightGBM-Based Prediction of Misalignments

by Kohei Arai, Jin Shimazoe and Mariko Oda

Information 2024, 15(8), 434; https://doi.org/10.3390/info15080434 - 26 Jul 2024

Abstract

Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which [...] Read more.

Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which is widely used as a classification problem solving method) to determine when the fluctuation exceeds the tolerable limit and what the acceptable range is. We also apply this to clarify the tolerable limit of fluctuation in the “Kurume Kasuri” pattern, which is unique to the Chikugo region of Japan, and devise a method to evaluate the fluctuation in real time when weaving the Kasuri and keep it within the acceptable range. This study proposes a method for maintaining a unique faded pattern of woven textiles by utilizing EfficientNet for classification, fine-tuned with Optuna, and LightGBM for predicting subtle misalignments. Our experiments show that EfficientNet achieves high performance in classifying the quality of unique faded patterns in woven textiles. Additionally, LightGBM demonstrates near-perfect accuracy in predicting subtle misalignments within the acceptable range for high-quality faded patterns by controlling the weaving thread tension. Consequently, this method effectively maintains the quality of Kurume Kasuri patterns within the desired criteria. Full article

(This article belongs to the Special Issue AI-Based Image Processing and Computer Vision)

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26 pages, 2258 KiB

Open AccessArticle

Dynamic Evolution Model of Internet Financial Public Opinion

by Chao Yu, Jianmin He, Qianting Ma and Xinyu Liu

Information 2024, 15(8), 433; https://doi.org/10.3390/info15080433 - 25 Jul 2024

Abstract

In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial [...] Read more.

In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial public opinions, including financial institutions, governments, media, and investors, and models the behavioral characteristics of these entities in the diffusion process. On this basis, we comprehensively use the multi-agent model and the SIR model to construct a dynamic evolution model of internet financial public opinion. We conduct a simulation analysis of the impact effects and interaction mechanisms of multi-agent behaviors in the financial market on the evolution of internet financial public opinion. The research results are as follows. Firstly, the financial institutions’ digitalization levels, government guidance, and the media authority positively promote the diffusion of internet financial public opinion. Secondly, the improvement of investors’ financial literacy can inhibit the diffusion of internet financial public opinion. Thirdly, under the interaction of multi-agent behaviors in the financial market, the effects of financial institutions’ digitalization level and investors’ financial literacy are more significant, while the effects of government guidance and media authority tend to converge. Full article

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30 pages, 351 KiB

Open AccessReview

AI in the Financial Sector: The Line between Innovation, Regulation and Ethical Responsibility

by Nurhadhinah Nadiah Ridzuan, Masairol Masri, Muhammad Anshari, Norma Latif Fitriyani and Muhammad Syafrudin

Information 2024, 15(8), 432; https://doi.org/10.3390/info15080432 - 25 Jul 2024

Abstract

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature [...] Read more.

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country. Full article

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

19 pages, 2603 KiB

Open AccessArticle

Development of a Metaverse Art Gallery of Image Chronicles (MAGIC) for Healthcare Education: A Digital Health Humanities Approach to Patients’ Medication Experiences

by Kevin Yi-Lwern Yap, Jayen Ho and Phylaine Shu Ting Toh

Information 2024, 15(8), 431; https://doi.org/10.3390/info15080431 - 25 Jul 2024

Abstract

Art therapy fosters emotional healing and growth. This process can offer healthcare professionals (HCPs) novel insights into patients’ medication experiences. We developed a Metaverse Art Gallery of Image Chronicles (MAGIC), which depicted patients’ medication experiences symbolically as hero–villain portrayals. This gallery aimed to [...] Read more.

Art therapy fosters emotional healing and growth. This process can offer healthcare professionals (HCPs) novel insights into patients’ medication experiences. We developed a Metaverse Art Gallery of Image Chronicles (MAGIC), which depicted patients’ medication experiences symbolically as hero–villain portrayals. This gallery aimed to enhance healthcare students’ learning through relatable insights into patients’ medication therapies. A character sheet was used to craft patients’ personifications of their medication experiences through an art-based narrative therapy approach. ChatGPT, NightCafe, Canva, HeyGen, and Camtasia were used to generate hero–villain portraits based on the character traits and mounted in MAGIC, which consisted of three virtual realms, each with a unique theme. Alpha-testing among sixteen Generation Z healthcare learners indicated that the content in MAGIC enabled them to understand the concepts of medication adherence (93.7%), art therapy (87.5%), and how patients related to their medications (81.3%). Perceived playfulness (r_s = 0.925, p < 0.001), perceived compatibility (r_s = 0.890, p < 0.001), and social norm (r_s = 0.862, p < 0.001) were strongly associated with their behavioral intention to adopt MAGIC as an educational platform. The learners enjoyed their experience (6.31 ± 0.70), felt that MAGIC was interactive and engaging (6.25 ± 0.78), and had the potential to be more effective than traditional learning methods (5.94 ± 0.93). Furthermore, they would recommend it to others for their education (5.94 ± 0.85). Full article

(This article belongs to the Special Issue Extended Reality: A New Way of Interacting with the World, 2nd Edition)

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21 pages, 3037 KiB

Open AccessArticle

Enhancing Accessibility to Analytics Courses in Higher Education through AI, Simulation, and e-Collaborative Tools

by Celia Osorio, Noelia Fuster, Wenwen Chen, Yangchongyi Men and Angel A. Juan

Information 2024, 15(8), 430; https://doi.org/10.3390/info15080430 - 25 Jul 2024

Abstract

This paper explores how the combination of artificial intelligence, simulation, and e-collaborative (AISEC) tools can support accessibility in analytics courses within higher education. In the era of online and blended learning, addressing the diverse needs of students with varying linguistic backgrounds and analytical [...] Read more.

This paper explores how the combination of artificial intelligence, simulation, and e-collaborative (AISEC) tools can support accessibility in analytics courses within higher education. In the era of online and blended learning, addressing the diverse needs of students with varying linguistic backgrounds and analytical proficiencies poses a significant challenge. This paper discusses how the combination of AISEC tools can contribute to mitigating barriers to accessibility for students undertaking analytics courses. Through a comprehensive review of existing literature and empirical insights from practical implementations, this paper shows the synergistic benefits of using AISEC tools for facilitating interactive engagement in analytics courses. Furthermore, the manuscript outlines practical strategies and best practices derived from real-world experiences carried out in different universities in Spain, Ireland, and Portugal. Full article

(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)

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20 pages, 305 KiB

Open AccessArticle

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

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

Information 2024, 15(8), 429; https://doi.org/10.3390/info15080429 - 24 Jul 2024

Abstract

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and [...] Read more.

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability. Full article

(This article belongs to the Special Issue Advances in High Performance Computing and Scalable Software)

21 pages, 3520 KiB

Open AccessArticle

Assessment of Published Papers on the Use of Machine Learning in Diagnosis and Treatment of Mastitis

by Maria V. Bourganou, Yiannis Kiouvrekis, Dimitrios C. Chatzopoulos, Sotiris Zikas, Angeliki I. Katsafadou, Dimitra V. Liagka, Natalia G. C. Vasileiou, George C. Fthenakis and Daphne T. Lianou

Information 2024, 15(8), 428; https://doi.org/10.3390/info15080428 - 24 Jul 2024

Abstract

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found [...] Read more.

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful. Full article

(This article belongs to the Special Issue 2nd Edition of Data Science for Health Services)

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19 pages, 2313 KiB

Open AccessArticle

Machine Translation for Open Scholarly Communication: Examining the Relationship between Translation Quality and Reading Effort

by Lieve Macken, Vanessa De Wilde and Arda Tezcan

Information 2024, 15(8), 427; https://doi.org/10.3390/info15080427 - 23 Jul 2024

Abstract

This study assesses the usability of machine-translated texts in scholarly communication, using self-paced reading experiments with texts from three scientific disciplines, translated from French into English and vice versa. Thirty-two participants, proficient in the target language, participated. This study uses three machine translation [...] Read more.

This study assesses the usability of machine-translated texts in scholarly communication, using self-paced reading experiments with texts from three scientific disciplines, translated from French into English and vice versa. Thirty-two participants, proficient in the target language, participated. This study uses three machine translation engines (DeepL, ModernMT, OpenNMT), which vary in translation quality. The experiments aim to determine the relationship between translation quality and readers’ reception effort, measured by reading times. The results show that for two disciplines, manual and automatic translation quality measures are significant predictors of reading time. For the most technical discipline, this study could not build models that outperformed the baseline models, which only included participant and text ID as random factors. This study acknowledges the need to include reader-specific features, such as prior knowledge, in future research. Full article

(This article belongs to the Special Issue Machine Translation for Conquering Language Barriers)

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24 pages, 667 KiB

Open AccessArticle

Utilizing Multi-Class Classification Methods for Automated Sleep Disorder Prediction

by Elias Dritsas and Maria Trigka

Information 2024, 15(8), 426; https://doi.org/10.3390/info15080426 - 23 Jul 2024

Abstract

Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to [...] Read more.

Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to control emotions and behaviour, reduces productivity at work, and can even increase stress or depression. In addition, poor sleep affects health; when sleep is insufficient, the chances of developing serious diseases greatly increase. Researchers in sleep medicine have identified an extensive list of sleep disorders, and thus leveraged Artificial Intelligence (AI) to automate their analysis and gain a deeper understanding of sleep patterns and related disorders. In this research, we seek a Machine Learning (ML) solution that will allow for efficient classification of unlabeled instances as being Sleep Apnea, Insomnia or Normal (subjects without a specific sleep disorder) by assessing the performance of two well-established strategies for multi-class classification tasks: the One-Vs-All (OVA) and One-Vs-One (OVO). In the context of the specific strategies, two well-known binary classification models were assumed, Logistic Regression (LR) and Support Vector Machines (SVMs). Both strategies’ validity was verified upon a dataset of diverse information related to the profiles (anthropometric data, sleep metrics, lifestyle and cardiovascular health factors) of potential patients or individuals not exhibiting any specific sleep disorder. Performance evaluation was carried out by comparing the weighted average results in all involved classes that represent these two specific sleep disorders and no-disorder occurrence; accuracy, kappa score, precision, recall, f-measure, and Area Under the ROC curve (AUC) were recorded and compared to identify an effective and robust model and strategy, both class-wise and on average. The experimental evaluation unveiled that after feature selection, 2-degree polynomial SVM under both strategies was the least complex and most efficient, recording an accuracy of 91.44%, a kappa score of 84.97%, precision, recall and f-measure equal to 0.914, and an AUC of 0.927. Full article

(This article belongs to the Special Issue Application of Machine Learning in Data Science and Computational Intelligence)

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26 pages, 590 KiB

Open AccessArticle

SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay

by Antonio Coscia, Andrea Iannacone, Antonio Maci and Alessandro Stamerra

Information 2024, 15(8), 425; https://doi.org/10.3390/info15080425 - 23 Jul 2024

Abstract

Reports produced by popular malware analysis services showed a disparity in samples available for different malware families. The unequal distribution between such classes can be attributed to several factors, such as technological advances and the application domain that seeks to infect a computer [...] Read more.

Reports produced by popular malware analysis services showed a disparity in samples available for different malware families. The unequal distribution between such classes can be attributed to several factors, such as technological advances and the application domain that seeks to infect a computer virus. Recent studies have demonstrated the effectiveness of deep learning (DL) algorithms when learning multi-class classification tasks using imbalanced datasets. This can be achieved by updating the learning function such that correct and incorrect predictions performed on the minority class are more rewarded or penalized, respectively. This procedure can be logically implemented by leveraging the deep reinforcement learning (DRL) paradigm through a proper formulation of the Markov decision process (MDP). This paper proposes SINNER, i.e., a DRL-based multi-class classifier that approaches the data imbalance problem at the algorithmic level by exploiting a redesigned reward function, which modifies the traditional MDP model used to learn this task. Based on the experimental results, the proposed formula appears to be successful. In addition, SINNER has been compared to several DL-based models that can handle class skew without relying on data-level techniques. Using three out of four datasets sourced from the existing literature, the proposed model achieved state-of-the-art classification performance. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Imbalanced Domains: Emerging Trends and Applications)

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14 pages, 738 KiB

Open AccessArticle

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

by Basma Al-Sabah and Gholamreza Anbarjafari

Information 2024, 15(8), 424; https://doi.org/10.3390/info15080424 - 23 Jul 2024

Abstract

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to [...] Read more.

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. Full article

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

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27 pages, 5633 KiB

Open AccessArticle

FILO: Automated FIx-LOcus Identification for Android Framework Compatibility Issues

by Marco Mobilio, Oliviero Riganelli, Daniela Micucci and Leonardo Mariani

Information 2024, 15(8), 423; https://doi.org/10.3390/info15080423 - 23 Jul 2024

Abstract

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, [...] Read more.

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps. Full article

(This article belongs to the Topic Software Engineering and Applications)

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17 pages, 1746 KiB

Open AccessArticle

Examining the Roles, Sentiments, and Discourse of European Interest Groups in the Ukrainian War through X (Twitter)

by Aritz Gorostiza-Cerviño, Álvaro Serna-Ortega, Andrea Moreno-Cabanillas, Ana Almansa-Martínez and Antonio Castillo-Esparcia

Information 2024, 15(7), 422; https://doi.org/10.3390/info15070422 - 22 Jul 2024

Abstract

This research focuses on examining the responses of interest groups listed in the European Transparency Register to the ongoing Russia–Ukraine war. Its aim is to investigate the nuanced reactions of 2579 commercial and business associations and 2957 companies and groups to the recent [...] Read more.

This research focuses on examining the responses of interest groups listed in the European Transparency Register to the ongoing Russia–Ukraine war. Its aim is to investigate the nuanced reactions of 2579 commercial and business associations and 2957 companies and groups to the recent conflict, as expressed through their X (Twitter) activities. Utilizing advanced text mining and NLP and LDA techniques, this study conducts a comprehensive analysis encompassing language dynamics, thematic shifts, sentiment variations, and activity levels exhibited by these entities both before and after the outbreak of the war. The results obtained reflect a gradual decrease in negative emotions regarding the conflict over time. Likewise, multiple forms of outside lobbying are identified in the communication strategies of interest groups. All in all, this empirical inquiry into how interest groups adapt their messaging in response to complex geopolitical events holds the potential to provide invaluable insights into the multifaceted role of lobbying in shapi ng public policies. Full article

(This article belongs to the Special Issue Information Processing in Multimedia Applications)

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15 pages, 473 KiB

Open AccessArticle

Semi-Supervised Learning for Multi-View Data Classification and Visualization

by Najmeh Ziraki, Alireza Bosaghzadeh and Fadi Dornaika

Information 2024, 15(7), 421; https://doi.org/10.3390/info15070421 - 22 Jul 2024

Abstract

Data visualization has several advantages, such as representing vast amounts of data and visually demonstrating patterns within it. Manifold learning methods help us estimate lower-dimensional representations of data, thereby enabling more effective visualizations. In data analysis, relying on a single view can often [...] Read more.

Data visualization has several advantages, such as representing vast amounts of data and visually demonstrating patterns within it. Manifold learning methods help us estimate lower-dimensional representations of data, thereby enabling more effective visualizations. In data analysis, relying on a single view can often lead to misleading conclusions due to its limited perspective. Hence, leveraging multiple views simultaneously and interactively can mitigate this risk and enhance performance by exploiting diverse information sources. Additionally, incorporating different views concurrently during the graph construction process using interactive visualization approach has improved overall performance. In this paper, we introduce a novel algorithm for joint consistent graph construction and label estimation. Our method simultaneously constructs a unified graph and predicts the labels of unlabeled samples. Furthermore, the proposed approach estimates a projection matrix that enables the prediction of labels for unseen samples. Moreover, it incorporates the information in the label space to further enhance the accuracy. In addition, it merges the information in different views along with the labels to construct a consensus graph. Experimental results conducted on various image databases demonstrate the superiority of our fusion approach compared to using a single view or other fusion algorithms. This highlights the effectiveness of leveraging multiple views and simultaneously constructing a unified graph for improved performance in data classification and visualization tasks in semi-supervised contexts. Full article

(This article belongs to the Special Issue Interactive Visualizations: Design, Technologies and Applications)

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20 pages, 2522 KiB

Open AccessArticle

Machine Learning-Driven Detection of Cross-Site Scripting Attacks

by Rahmah Alhamyani and Majid Alshammari

Information 2024, 15(7), 420; https://doi.org/10.3390/info15070420 - 20 Jul 2024

Abstract

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) [...] Read more.

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) and false-negative (FN) rates. This research proposes a novel machine learning (ML)-based approach for robust XSS attack detection. We evaluate various models including Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVMs), Decision Trees (DTs), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning. The models are trained on a real-world dataset categorized into benign and malicious traffic, incorporating feature selection methods like Information Gain (IG) and Analysis of Variance (ANOVA) for optimal performance. Our findings reveal exceptional accuracy, with the RF model achieving 99.78% and ensemble models exceeding 99.64%. These results surpass existing methods, demonstrating the effectiveness of the proposed approach in securing web applications while minimizing FPs and FNs. This research offers a significant contribution to the field of web application security by providing a highly accurate and robust ML-based solution for XSS attack detection. Full article

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30 pages, 12265 KiB

Open AccessArticle

Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges

by Hamed Alshammari and Khaled Elleithy

Information 2024, 15(7), 419; https://doi.org/10.3390/info15070419 - 19 Jul 2024

Abstract

Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text detection models using Transformer-based pre-trained models, [...] Read more.

Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text detection models using Transformer-based pre-trained models, specifically AraELECTRA, AraBERT, XLM-R, and mBERT. Our primary goal is to detect AIGTs in essays and overcome the challenges posed by the diacritics that usually appear in Arabic religious texts. We created several novel datasets with diacritized and non-diacritized texts comprising up to 9666 HWT and AIGT training examples. We aimed to assess the robustness and effectiveness of the detection models on out-of-domain (OOD) datasets to assess their generalizability. Our detection models trained on diacritized examples achieved up to 98.4% accuracy compared to GPTZero’s 62.7% on the AIRABIC benchmark dataset. Our experiments reveal that, while including diacritics in training enhances the recognition of the diacritized HWTs, duplicating examples with and without diacritics is inefficient despite the high accuracy achieved. Applying a dediacritization filter during evaluation significantly improved model performance, achieving optimal performance compared to both GPTZero and the detection models trained on diacritized examples but evaluated without dediacritization. Although our focus was on Arabic due to its writing challenges, our detector architecture is adaptable to any language. Full article

(This article belongs to the Section Artificial Intelligence)

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13 pages, 2487 KiB

Open AccessArticle

SiamSMN: Siamese Cross-Modality Fusion Network for Object Tracking

by Shuo Han, Lisha Gao, Yue Wu, Tian Wei, Manyu Wang and Xu Cheng

Information 2024, 15(7), 418; https://doi.org/10.3390/info15070418 - 19 Jul 2024

Abstract

The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel Siamese network [...] Read more.

The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel Siamese network for visual object tracking, named SiamSMN, which consists of a feature extraction network, a multi-scale fusion module, and a prediction head. First, the feature extraction network is used to extract the features of the template image and the search image, which is calculated by a depth-wise cross-correlation operation to produce multiple similarity feature maps. Second, we propose an effective multi-scale fusion module that can extract global context information for object search and learn the interdependencies between multi-level similarity maps. In addition, to further improve tracking accuracy, we design a learnable prediction head module to generate a boundary point for each side based on the coarse bounding box, which can solve the problem of inconsistent classification and regression during the tracking. Extensive experiments on four public benchmarks demonstrate that the proposed tracker has a competitive performance among other state-of-the-art trackers. Full article

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

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15 pages, 3305 KiB

Open AccessArticle

Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images

by Haotian Wang, Aleksandar Vakanski, Changfa Shi and Min Xian

Information 2024, 15(7), 417; https://doi.org/10.3390/info15070417 - 18 Jul 2024

Abstract

Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is limited. To address this issue, we propose a novel multitask learning network with [...] Read more.

Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is limited. To address this issue, we propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances generalization by learning shared representation from the following three tasks: instance segmentation, nuclei distance map prediction, and overlapped nuclei distance map prediction. The proposed bending loss defines high penalties to concave contour points with large curvatures, and small penalties are applied to convex contour points with small curvatures. Minimizing the bending loss avoids generating contours that encompass multiple nuclei. In addition, two new quantitative metrics, the Aggregated Jaccard Index of overlapped nuclei (AJIO) and the accuracy of overlapped nuclei (ACCO), have been designed to evaluate overlapped nuclei segmentation. We validate the proposed approach on the CoNSeP and MoNuSegv1 data sets using the following seven quantitative metrics: Aggregate Jaccard Index, Dice, Segmentation Quality, Recognition Quality, Panoptic Quality, AJIO, and ACCO. Extensive experiments demonstrate that the proposed Bend-Net outperforms eight state-of-the-art approaches. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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16 pages, 256 KiB

Open AccessArticle

Higher Education Students’ Perceptions of GenAI Tools for Learning

by Wajeeh Daher and Asma Hussein

Information 2024, 15(7), 416; https://doi.org/10.3390/info15070416 - 18 Jul 2024

Abstract

Students’ perceptions of tools with which they learn affect the outcomes of this learning. GenAI tools are new tools that have promise for students’ learning, especially higher education students. Examining students’ perceptions of GenAI tools as learning tools can help instructors better plan [...] Read more.

Students’ perceptions of tools with which they learn affect the outcomes of this learning. GenAI tools are new tools that have promise for students’ learning, especially higher education students. Examining students’ perceptions of GenAI tools as learning tools can help instructors better plan activities that utilize these tools in the higher education context. The present research considers four components of students’ perceptions of GenAI tools: efficiency, interaction, affect, and intention. To triangulate data, it combines the quantitative and the qualitative methodologies, by using a questionnaire and by conducting interviews. A total of 153 higher education students responded to the questionnaire, while 10 higher education students participated in the interview. The research results indicated that the means of affect, interaction, and efficiency were significantly medium, while the mean of intention was significantly high. The research findings showed that in efficiency, affect, and intention, male students had significantly higher perceptions of AI tools than female students, but in the interaction component, the two genders did not differ significantly. Moreover, the degree affected only the perception of interaction of higher education students, where the mean value of interaction was significantly different between B.A. and Ph.D. students in favor of Ph.D. students. Moreover, medium-technology-knowledge and high-technology-knowledge students differed significantly in their perceptions of working with AI tools in the interaction component only, where this difference was in favor of the high-technology-knowledge students. Furthermore, AI knowledge significantly affected efficiency, interaction, and affect of higher education students, where they were higher in favor of high-AI-knowledge students over low-AI-knowledge students, as well as in favor of medium-AI-knowledge students over low-AI-knowledge students. Full article

(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)

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