Information

17 pages, 6436 KiB

Open AccessArticle

One-Shot Learning from Prototype Stock Keeping Unit Images

by Aleksandra Kowalczyk and Grzegorz Sarwas

Information 2024, 15(9), 526; https://doi.org/10.3390/info15090526 - 28 Aug 2024

Viewed by 138

This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning [...] Read more.

This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning techniques mitigate this issue by enabling classification from a single prototype image per product class, thus reducing data annotation efforts. We introduce the Variational Prototyping Encoder (VPE), a novel deep neural network for one-shot classification. Utilizing a support set of prototype SKU images, VPE learns to classify query images by capturing image similarity and prototypical concepts. Unlike metric learning-based approaches, VPE pre-learns image translation from real-world object images to prototype images as a meta-task, facilitating efficient one-shot classification with minimal supervision. Our research demonstrates that VPE effectively reduces the need for extensive datasets by utilizing a single image per class while accurately classifying query images into their respective categories, thus providing a practical solution for product classification tasks. Full article

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

31 pages, 12305 KiB

Open AccessArticle

Living in the Age of Deepfakes: A Bibliometric Exploration of Trends, Challenges, and Detection Approaches

by Adrian Domenteanu, George-Cristian Tătaru, Liliana Crăciun, Anca-Gabriela Molănescu, Liviu-Adrian Cotfas and Camelia Delcea

Information 2024, 15(9), 525; https://doi.org/10.3390/info15090525 - 28 Aug 2024

Viewed by 418

Abstract

In an era where all information can be reached with one click and by using the internet, the risk has increased in a significant manner. Deepfakes are one of the main threats on the internet, and affect society by influencing and altering information, [...] Read more.

In an era where all information can be reached with one click and by using the internet, the risk has increased in a significant manner. Deepfakes are one of the main threats on the internet, and affect society by influencing and altering information, decisions, and actions. The rise of artificial intelligence (AI) has simplified the creation of deepfakes, allowing even novice users to generate false information in order to create propaganda. One of the most prevalent methods of falsification involves images, as they constitute the most impactful element with which a reader engages. The second most common method pertains to videos, which viewers often interact with. Two major events led to an increase in the number of deepfake images on the internet, namely the COVID-19 pandemic and the Russia–Ukraine conflict. Together with the ongoing “revolution” in AI, deepfake information has expanded at the fastest rate, impacting each of us. In order to reduce the risk of misinformation, users must be aware of the deepfake phenomenon they are exposed to. This also means encouraging users to more thoroughly consider the sources from which they obtain information, leading to a culture of caution regarding any new information they receive. The purpose of the analysis is to extract the most relevant articles related to the deepfake domain. Using specific keywords, a database was extracted from Clarivate Analytics’ Web of Science Core Collection. Given the significant annual growth rate of 161.38% and the relatively brief period between 2018 and 2023, the research community demonstrated keen interest in the issue of deepfakes, positioning it as one of the most forward-looking subjects in technology. This analysis aims to identify key authors, examine collaborative efforts among them, explore the primary topics under scrutiny, and highlight major keywords, bigrams, or trigrams utilized. Additionally, this document outlines potential strategies to combat the proliferation of deepfakes in order to preserve information trust. Full article

(This article belongs to the Special Issue Beyond Detection: Disinformation and the Amplification of Toxic Content in the Age of Social Media)

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16 pages, 5090 KiB

Open AccessArticle

Method for Landslide Area Detection Based on EfficientNetV2 with Optical Image Converted from SAR Image Using pix2pixHD with Spatial Attention Mechanism in Loss Function

by Kohei Arai, Yushin Nakaoka and Hiroshi Okumura

Information 2024, 15(9), 524; https://doi.org/10.3390/info15090524 - 28 Aug 2024

Viewed by 171

Abstract

A method for landslide area detection based on EfficientNetV2 with optical image converted from SAR image using pix2pixHD with a spatial attention mechanism in the loss function is proposed. Meteorological landslides such as landslides after heavy rains occur regardless of day or night [...] Read more.

A method for landslide area detection based on EfficientNetV2 with optical image converted from SAR image using pix2pixHD with a spatial attention mechanism in the loss function is proposed. Meteorological landslides such as landslides after heavy rains occur regardless of day or night and weather conditions. Meteorological landslides such as landslides are easier to visually judge using optical images than SAR images, but optical images cannot be observed at night, in the rain, or on cloudy days. Therefore, we devised a method to convert SAR images, which allow all-weather observation regardless of day or night, into optical images using pix2pixHD, and to learn about landslide areas using the converted optical images to build a trained model. We used SAR and optical images derived from Sentinel-1 and -2, which captured landslides caused by the earthquake on 14 April 2016, as training data, and constructed a learning model that classifies landslide areas using EfficientNetV2. We evaluated the superiority of the proposed method by comparing it with a learning model that uses only SAR images. As a result, it was confirmed that the F1-score and AUC were 0.3396 and 0.2697, respectively, when using only SAR images, but were improved by 1.52 to 1.84 times to 0.6250 and 0.4109, respectively, when using the proposed method. Full article

(This article belongs to the Section Information Applications)

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16 pages, 3993 KiB

Open AccessArticle

Computational Analysis of Marker Genes in Alzheimer’s Disease across Multiple Brain Regions

by Panagiotis Karanikolaos, Marios G. Krokidis, Themis P. Exarchos and Panagiotis Vlamos

Information 2024, 15(9), 523; https://doi.org/10.3390/info15090523 - 27 Aug 2024

Viewed by 282

Abstract

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the [...] Read more.

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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12 pages, 373 KiB

Open AccessArticle

Research on Optimizing Human Resource Expenditure in the Allocation of Materials in Universities

by Li Zhao and Ying Wang

Information 2024, 15(9), 522; https://doi.org/10.3390/info15090522 - 27 Aug 2024

Viewed by 231

Abstract

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, [...] Read more.

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies. Full article

(This article belongs to the Section Information Applications)

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18 pages, 2293 KiB

Open AccessArticle

Social Media Topic Classification on Greek Reddit

by Charalampos Mastrokostas, Nikolaos Giarelis and Nikos Karacapilidis

Information 2024, 15(9), 521; https://doi.org/10.3390/info15090521 - 26 Aug 2024

Viewed by 237

Abstract

Text classification (TC) is a subtask of natural language processing (NLP) that categorizes text pieces into predefined classes based on their textual content and thematic aspects. This process typically includes the training of a Machine Learning (ML) model on a labeled dataset, where [...] Read more.

Text classification (TC) is a subtask of natural language processing (NLP) that categorizes text pieces into predefined classes based on their textual content and thematic aspects. This process typically includes the training of a Machine Learning (ML) model on a labeled dataset, where each text example is associated with a specific class. Recent progress in Deep Learning (DL) enabled the development of deep neural transformer models, surpassing traditional ML ones. In any case, works of the topic classification literature prioritize high-resource languages, particularly English, while research efforts for low-resource ones, such as Greek, are limited. Taking the above into consideration, this paper presents: (i) the first Greek social media topic classification dataset; (ii) a comparative assessment of a series of traditional ML models trained on this dataset, utilizing an array of text vectorization methods including TF-IDF, classical word and transformer-based Greek embeddings; (iii) a fine-tuned GREEK-BERT-based TC model on the same dataset; (iv) key empirical findings demonstrating that transformer-based embeddings significantly increase the performance of traditional ML models, while our fine-tuned DL model outperforms previous ones. The dataset, the best-performing model, and the experimental code are made public, aiming to augment the reproducibility of this work and advance future research in the field. Full article

(This article belongs to the Section Artificial Intelligence)

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38 pages, 6519 KiB

Open AccessArticle

Digital Developmental Advising Systems for Engineering Students Based on Accreditation Board of Engineering and Technology Student Outcome Evaluations

by Wajid Hussain, Mak Fong and William G. Spady

Information 2024, 15(9), 520; https://doi.org/10.3390/info15090520 - 26 Aug 2024

Viewed by 256

Abstract

The purpose of this research is to examine the benefits and limitations of the implementation of novel digital academic advising systems based on the principles of authentic outcome-based education (OBE) using automated collection and reporting processes for Accreditation Board for Engineering and Technology [...] Read more.

The purpose of this research is to examine the benefits and limitations of the implementation of novel digital academic advising systems based on the principles of authentic outcome-based education (OBE) using automated collection and reporting processes for Accreditation Board for Engineering and Technology (ABET) student outcomes data for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools^® 6.0, including an advising module based on assessment methodology incorporating the faculty course assessment report, performance indicators, and hybrid rubrics classified according to the affective, cognitive, and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of (a) detailed students’ outcomes and performance indicator information and (b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the faculty course assessment report using performance indicators and hybrid rubrics can provide comprehensive and realistic outcome data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions, and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better-performing outcomes to reinforce their natural affinity for learning specialized competencies to help them pursue related and successful career paths. Full article

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

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12 pages, 2987 KiB

Open AccessArticle

A Lightweight Crop Pest Detection Method Based on Improved RTMDet

by Wanqing Wang and Haoyue Fu

Information 2024, 15(9), 519; https://doi.org/10.3390/info15090519 - 26 Aug 2024

Viewed by 244

Abstract

To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest detection. First, the real-time [...] Read more.

To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest detection. First, the real-time object detection network RTMDet is utilized to design the pest detection model. Then, the backbone and neck structures are pruned to reduce the number of parameters and computation. Subsequently, a shortcut connection module is added to the classification and regression branches, respectively, to enhance its feature learning capability, thereby improving its accuracy. Experimental results show that, compared to the original model RTMDet, the improved model RTMDet++ reduces the number of parameters by 15.5%, the computation by 25.0%, and improves the mean average precision by 0.3% on the crop pest dataset IP102. The improved model RTMDet++ achieves a mAP of 94.1%, a precision of 92.5%, and a recall of 92.7% with 4.117M parameters and 3.130G computations, outperforming other object detection methods. The proposed model RTMDet++ achieves higher performance with fewer parameters and computations, which can be applied to crop pest detection in practice and aids in pest control research. Full article

(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)

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18 pages, 6694 KiB

Open AccessArticle

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

by Ching-Chang Wong, Kun-Duo Weng and Bo-Yun Yu

Information 2024, 15(9), 518; https://doi.org/10.3390/info15090518 - 26 Aug 2024

Viewed by 349

Abstract

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to [...] Read more.

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions. Full article

(This article belongs to the Special Issue Advanced Control Topics on Robotic Vehicles)

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34 pages, 786 KiB

Open AccessReview

Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

by Ibomoiye Domor Mienye, Theo G. Swart and George Obaido

Information 2024, 15(9), 517; https://doi.org/10.3390/info15090517 - 25 Aug 2024

Viewed by 1200

Abstract

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, [...] Read more.

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)

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30 pages, 1356 KiB

Open AccessArticle

Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia

by Ali Louati, Hassen Louati, Meshal Alharbi, Elham Kariri, Turki Khawaji, Yasser Almubaddil and Sultan Aldwsary

Information 2024, 15(9), 516; https://doi.org/10.3390/info15090516 - 23 Aug 2024

Viewed by 580

Abstract

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained [...] Read more.

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond. Full article

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25 pages, 406 KiB

Open AccessSystematic Review

Virtual and Augmented Reality in Science, Technology, Engineering, and Mathematics (STEM) Education: An Umbrella Review

by Yiqun Zhang, Miguel A. Feijoo-Garcia, Yiyin Gu, Voicu Popescu, Bedrich Benes and Alejandra J. Magana

Information 2024, 15(9), 515; https://doi.org/10.3390/info15090515 - 23 Aug 2024

Viewed by 441

Abstract

The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and [...] Read more.

The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)

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21 pages, 6090 KiB

Open AccessArticle

Towards an Urban Planning Scenario Model System—A Tool for Exploring Urban Uncertainty: A Case Study of Diaozhen, China

by Xuefei Li, Liang Zhao, Yang Yang, Danni Liu, Baizhen Li and Chunlu Liu

Information 2024, 15(9), 514; https://doi.org/10.3390/info15090514 - 23 Aug 2024

Viewed by 278

Abstract

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss [...] Read more.

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss the assumptions and theoretical basis of the model system. The model system will be driven by scenario planning theory and sustainable urban development principles. It will export land-use planning based on selected urban development scenarios and urban planning theories. This paper takes Diao Town in Jinan as an example. Applying GIS spatial analysis and hierarchical analysis, this paper determines the suitability of land use and the weights of different influencing factors, combined with the land-use conflict identification model, for land-use decision-making. Finally, the assessment module verifies whether the planning scheme complies with laws and regulations to achieve an active, reactive response to uncertainty. The paper discusses the ‘uncertainty’ of urban planning and proposes a creative, flexible, and timely planning platform that allows planners and other participants to model and visualize their scenarios. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis II)

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15 pages, 549 KiB

Open AccessArticle

A Hybrid Hierarchical Mathematical Heuristic Solution of Sparse Algebraic Equations in Sentiment Analysis

by Maryam Jalali, Morteza Zahedi, Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado and João Manuel R. S. Tavares

Information 2024, 15(9), 513; https://doi.org/10.3390/info15090513 - 23 Aug 2024

Viewed by 305

Abstract

Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster [...] Read more.

Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection. Full article

(This article belongs to the Special Issue 2nd Edition of Information Retrieval and Social Media Mining)

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19 pages, 2187 KiB

Open AccessArticle

Towards an Innovative Model for Cybersecurity Awareness Training

by Hamed Taherdoost

Information 2024, 15(9), 512; https://doi.org/10.3390/info15090512 - 23 Aug 2024

Viewed by 474

Abstract

The rapid evolution of cybersecurity threats poses a significant challenge to organizations and individuals, necessitating strengthening defense mechanisms against malicious operations. Amidst this ever-changing environment, the importance of implementing efficacious cybersecurity awareness training has escalated dramatically. This paper presents the Integrated Cybersecurity Awareness [...] Read more.

The rapid evolution of cybersecurity threats poses a significant challenge to organizations and individuals, necessitating strengthening defense mechanisms against malicious operations. Amidst this ever-changing environment, the importance of implementing efficacious cybersecurity awareness training has escalated dramatically. This paper presents the Integrated Cybersecurity Awareness Training (iCAT) model, which leverages knowledge graphs, serious games, and gamification to enhance cybersecurity training. The iCAT model’s micro-learning module increases flexibility and accessibility, while real-time progress monitoring and adaptive feedback ensure effective learning outcomes. Evaluations show improved participant engagement and knowledge retention, making iCAT a practical and efficient solution for cybersecurity challenges. With an emphasis on adaptability and applicability, iCAT provides organizations in search of accessible and efficient cybersecurity awareness training with a streamlined approach. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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12 pages, 583 KiB

Open AccessArticle

IKDD: A Keystroke Dynamics Dataset for User Classification

by Ioannis Tsimperidis, Olga-Dimitra Asvesta, Eleni Vrochidou and George A. Papakostas

Information 2024, 15(9), 511; https://doi.org/10.3390/info15090511 - 23 Aug 2024

Viewed by 312

Abstract

Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state [...] Read more.

Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility. Full article

(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification)

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27 pages, 1797 KiB

Open AccessArticle

Assessment of Customers’ Evaluations of Service Quality in Live-Streaming Commerce: Conceptualizing and Testing a Multidimensional and Hierarchical Model

by Chaang-Iuan Ho, Yaoyu Liu and Ming-Chih Chen

Information 2024, 15(9), 510; https://doi.org/10.3390/info15090510 - 23 Aug 2024

Viewed by 317

Abstract

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs [...] Read more.

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs and improving service quality have become the key issues for survival. This study aims to develop and empirically validate a multidimensional hierarchical model for measuring service quality on LSC platforms. A hierarchical reflective construct was proposed to capture dimensions based on the literature on e-retail and social commerce service quality. The proposed model was rigorously tested using two waves of survey data through the partial least squares method. Results showed that the service quality of LSC is a third-order, reflective construct and includes five primary dimensions (the streamer’s interaction quality, physical environment, website quality, outcome quality, and ordering process) and twelve sub-dimensions (trustworthiness, expertise, responsiveness, telepresence, consumption scenarios, information quality, system operation quality, fulfillment and refund/compensation, privacy/security, contact, and ease of use). Findings also supported the hypothesis that service quality has a significant impact on customers’ satisfaction and their behavioral intentions. Furthermore, we tested an alternative model, and the results showed that the relationship between dimensions and overall assessment is reflective rather than formative. We offered directions for further research on LSC service quality and discussed managerial implications stemming from the empirical findings. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")

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