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Feature Papers in Information in 2023
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Feature Papers in Information in 2023

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 64003

Special Issue Editors

Prof. Dr. Willy Susilo

E-Mail Website
Guest Editor
School of Computer Science and Software Engineering, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Interests: cryptography; computer security; design of signature schemes
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Hu

E-Mail Website
Guest Editor
Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: information systems; information applications; cyber-physical systems; social computing; interaction design; human computer interaction; internet of things
Prof. Dr. Antonio Jiménez-Martín

E-Mail Website
Guest Editor
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Boadilla del Monte, 28660 Madrid, Spain
Interests: multi-attribute utility theory, group decision making; preference quantification; metaheuristics; simulation, risk analysis and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zahir M. Hussain

E-Mail Website
Guest Editor
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: signal processing; artificial intelligence; electronics; sensors; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Editor-in-Chief and Editorial Board Members of Information, we are pleased to announce the Special Issue entitled "Feature Papers in Information in 2022". This Special Issue will be a collection of high-quality papers from Editorial Board Members and leading researchers invited by the Editorial Office. Both original research articles and comprehensive review papers are welcome. All topics related to information processing in various fields and applications are welcome.

Prof. Dr. Willy Susilo
Dr. Jun Hu
Prof. Dr. Antonio Jiménez-Martín
Prof. Dr. Zahir M. Hussain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (33 papers)

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20 pages, 4383 KiB  
Article
Synthetic Displays and Their Potential for Driver Assistance Systems
by Elisabeth Maria Wögerbauer, Christoph Bernhard and Heiko Hecht
Information 2024, 15(4), 177; https://doi.org/10.3390/info15040177 - 23 Mar 2024
Viewed by 616
Abstract
Advanced visual display technologies typically supplement the out-of-window view with separate displays (e.g., analog speedometer or artificial horizon) or with overlays (e.g., projected speedometer or map). Studies on head-up displays suggest that altering the out-of-window view itself is superior to supplemental displays, as [...] Read more.
Advanced visual display technologies typically supplement the out-of-window view with separate displays (e.g., analog speedometer or artificial horizon) or with overlays (e.g., projected speedometer or map). Studies on head-up displays suggest that altering the out-of-window view itself is superior to supplemental displays, as sensor-based information not normally visible to the driver can be included. Such novel synthetic displays have been researched for cockpit implementation but less so for driving. We discuss such view-altering synthetic displays in general, and camera–monitor systems (CMS) designed to replace rear-view mirrors as a special instance of a novel synthetic display in the automotive domain. In a standard CMS, a camera feed is presented on a monitor, but could also be integrated into the windshield of the car. More importantly, the camera feed can undergo alterations, augmentations, or condensations before being displayed. The implications of these technologies are discussed, along with findings from an experiment examining the impact of information reduction on a time-to-contact (TTC) estimation task. In this experiment, observers judged the TTC of approaching cars based on the synthetic display of a futuristic CMS. Promisingly, TTC estimations were unaffected by information reduction. The study also emphasizes the significance of the visual reference frame. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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24 pages, 961 KiB  
Article
Multi-Objective Advantage Actor-Critic Algorithm for Hybrid Disassembly Line Balancing with Multi-Skilled Workers
by Jiacun Wang, Guipeng Xi, Xiwang Guo, Shujin Qin and Henry Han
Information 2024, 15(3), 168; https://doi.org/10.3390/info15030168 - 19 Mar 2024
Viewed by 751
Abstract
The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In [...] Read more.
The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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23 pages, 4329 KiB  
Article
Single-Frame-Based Data Compression for CAN Security
by Shi-Yi Jin, Dong-Hyun Seo, Yeon-Jin Kim, Yong-Eun Kim, Samuel Woo and Jin-Gyun Chung
Information 2024, 15(3), 132; https://doi.org/10.3390/info15030132 - 28 Feb 2024
Viewed by 887
Abstract
To authenticate a controller area network (CAN) data frame, a message authentication code (MAC) must be sent along with the CAN frame, but there is no space reserved for the MAC in the CAN frame. Recently, difference-based compression (DBC) algorithms have been used [...] Read more.
To authenticate a controller area network (CAN) data frame, a message authentication code (MAC) must be sent along with the CAN frame, but there is no space reserved for the MAC in the CAN frame. Recently, difference-based compression (DBC) algorithms have been used to create a space inside the frame. DBC has the advantage of being very efficient, but its drawback is that, if an error occurs in one frame, the effects of that error propagate to subsequent frames. In this paper, a CAN data compression algorithm is proposed that compresses the current frame without relying on previous frames. Therefore, an error generated in one frame cannot be propagated to subsequent frames. In addition, a CAN signal grouping technique is proposed based on entropy analysis. To efficiently authenticate CAN frames, the length of the compressed data must be 4 bytes or less (4BL). Simulation shows that the 4BL-compression ratio of a Kia Sorento vehicle is 99.36% in the DBC method, but 100% in the proposed method. In an LS Mtron tractor, the 4BL-compression ratio is 98.58% in the DBC method, but 100% in the proposed method. In addition, the execution time of the proposed compression algorithm is only 27.39% of that of the DBC algorithm. The results show that the proposed algorithm has better compression characteristics for CAN security than the DBC algorithms. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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22 pages, 1525 KiB  
Article
Information Systems Strategy for Multi-National Corporations: Towards an Operational Model and Action List
by Martin Wynn and Christian Weber
Information 2024, 15(2), 119; https://doi.org/10.3390/info15020119 - 18 Feb 2024
Viewed by 1276
Abstract
The development and implementation of information systems strategy in multi-national corporations (MNCs) faces particular challenges—cultural differences and variations in work values and practices across different countries, numerous technology landscapes and legacy issues, language and accounting particularities, and differing business models. This article builds [...] Read more.
The development and implementation of information systems strategy in multi-national corporations (MNCs) faces particular challenges—cultural differences and variations in work values and practices across different countries, numerous technology landscapes and legacy issues, language and accounting particularities, and differing business models. This article builds upon the existing literature and in-depth interviews with eighteen industry practitioners employed in six MNCs to construct an operational model to address these challenges. The research design is based on an inductive, qualitative approach that develops an initial conceptual framework—derived from the literature—into an operational model, which is then applied and refined in a case study company. The final model consists of change components and process phases. Six change components are identified that drive and underpin IS strategy—business strategy, systems projects, technology infrastructure, process change, skills and competencies, and costs and benefits. Five core process phases are recognized—review, align, engage, execute, and control. The model is based on the interaction between these two dimensions—change components and process phases—and an action list is also developed to support the application of the model, which contributes to the theory and practice of information systems deployment in MNCs. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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19 pages, 980 KiB  
Article
AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis
by Dimitris Mpouziotas, Jeries Besharat, Ioannis G. Tsoulos and Chrysostomos Stylios
Information 2024, 15(2), 83; https://doi.org/10.3390/info15020083 - 04 Feb 2024
Viewed by 936
Abstract
AliAmvra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf (GP) to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action to form a business identity with high added value and achieve [...] Read more.
AliAmvra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf (GP) to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action to form a business identity with high added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this study is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmvra project, with a target goal of improving customer experience and product quality. An extensive analysis was conducted for this study by collecting data through surveys that took place in the gastronomes of the AliAmvra project. Our objective was to conduct two types of reviews, one focused in data analysis and the other on evaluating model-driven algorithms. Each review utilized a survey with an individual structure, with each one serving a different purpose. In addition, our model review focused its attention on developing a robust recommendation system with said data. The algorithms we evaluated were MLP (multi-layered perceptron), RBF (radial basis function), GenClass, NNC (neural network construction), and FC (feature construction), which were used for the implementation of the recommendation system. As our final verdict, we determined that FC (feature construction) performed best, presenting the lowest classification rate of 24.87%, whilst the algorithm that performed the worst on average was RBF (radial basis function). Our final objective was to showcase and expand the work put into the AliAmvra project through this analysis. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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14 pages, 944 KiB  
Article
A Fast Intersection of Confidence Intervals Method-Based Adaptive Thresholding for Sparse Image Reconstruction Using the Matrix Form of the Wavelet Transform
by Ivan Volaric and Victor Sucic
Information 2024, 15(2), 71; https://doi.org/10.3390/info15020071 - 24 Jan 2024
Viewed by 1077
Abstract
One of the frequently used classes of sparse reconstruction algorithms is based on the iterative shrinkage/thresholding procedure, in which the thresholding parameter controls a trade-off between the algorithm’s accuracy and execution time. In order to avoid this trade-off, we propose using a fast [...] Read more.
One of the frequently used classes of sparse reconstruction algorithms is based on the iterative shrinkage/thresholding procedure, in which the thresholding parameter controls a trade-off between the algorithm’s accuracy and execution time. In order to avoid this trade-off, we propose using a fast intersection of confidence intervals method in order to adaptively control the threshold value throughout the iterations of the reconstruction algorithm. We have upgraded the two-step iterative shrinkage thresholding algorithm with a such procedure, improving its performance. The proposed algorithm, denoted as the FICI-TwIST, along with a few selected state-of-the-art sparse reconstruction algorithms, has been tested on the classical problem of image recovery by emphasizing the image sparsity in the discrete cosine and the discrete wavelet domain. Furthermore, we have derived a single wavelet transformation matrix which avoids wrapping effects, thereby achieving significantly faster execution times as compared to a more traditional function-based transformation. The obtained results indicate the competitive performance of the proposed algorithm, even in cases where all algorithm parameters have been individually fine-tuned for best performance. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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17 pages, 4757 KiB  
Article
Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification: A Case Study for COVID-19
by Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul
Information 2024, 15(1), 58; https://doi.org/10.3390/info15010058 - 18 Jan 2024
Cited by 1 | Viewed by 1602
Abstract
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, [...] Read more.
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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16 pages, 3143 KiB  
Article
KEGGSum: Summarizing Genomic Pathways
by Chaim David and Haridimos Kondylakis
Information 2024, 15(1), 56; https://doi.org/10.3390/info15010056 - 17 Jan 2024
Viewed by 1163
Abstract
Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that depict the [...] Read more.
Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that depict the relationships between the diverse items participating in biological procedures, such as genes and chemical compounds. However, the size, complexity, and diversity of these graphs make them difficult to explore and understand, as well as making it difficult to extract a clear conclusion regarding their most important components. In this regard, we present KEGGSum, a system enabling the efficient and effective summarization of KEGG pathways. KEGGSum receives a KEGG identifier (Kid) as an input, connects to the KEGG database, downloads a specialized form of the pathway, and determines the most important nodes in the graph. To identify the most important nodes in the KEGG graphs, we explore multiple centrality measures that have been proposed for generic graphs, showing their applicability to KEGG graphs as well. Then, we link the selected nodes in order to produce a summary graph out of the initial KEGG graph. Finally, our system visualizes the generated summary, enabling an understanding of the most important parts of the initial graph. We experimentally evaluate our system, and we show its advantages and benefits. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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28 pages, 1639 KiB  
Article
IT Risk Management: Towards a System for Enhancing Objectivity in Asset Valuation That Engenders a Security Culture
by Bilgin Metin, Sefa Duran, Eda Telli, Meltem Mutlutürk and Martin Wynn
Information 2024, 15(1), 55; https://doi.org/10.3390/info15010055 - 17 Jan 2024
Viewed by 1597
Abstract
In today’s technology-centric business environment, where organizations encounter numerous cyber threats, effective IT risk management is crucial. An objective risk assessment—based on information relating to business requirements, human elements, and the security culture within an organisation—can provide a sound basis for informed decision [...] Read more.
In today’s technology-centric business environment, where organizations encounter numerous cyber threats, effective IT risk management is crucial. An objective risk assessment—based on information relating to business requirements, human elements, and the security culture within an organisation—can provide a sound basis for informed decision making, effective risk prioritisation, and the implementation of suitable security measures. This paper focuses on asset valuation, supply chain risk, and enhanced objectivity—via a “segregation of duties” approach—to extend and apply the capabilities of an established security culture framework. The resultant system design aims at mitigating subjectivity in IT risk assessments, thereby diminishing personal biases and presumptions to provide a more transparent and accurate understanding of the real risks involved. Survey responses from 16 practitioners working in the private and public sectors confirmed the validity of the approach but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate. This research contributes to the literature on IT and cyber risk management and provides new perspectives on the need to improve objectivity in asset valuation and risk assessment. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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18 pages, 2897 KiB  
Article
Is Short-Term Memory Made of Two Processing Units? Clues from Italian and English Literatures down Several Centuries
by Emilio Matricciani
Information 2024, 15(1), 6; https://doi.org/10.3390/info15010006 - 20 Dec 2023
Viewed by 1088
Abstract
We propose that short-term memory (STM), when processing a sentence, uses two independent units in series. The clues for conjecturing this model emerge from studying many novels from Italian and English Literature. This simple model, referring to the surface of language, seems to [...] Read more.
We propose that short-term memory (STM), when processing a sentence, uses two independent units in series. The clues for conjecturing this model emerge from studying many novels from Italian and English Literature. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels. Therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in the human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7 ± 2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process a sentence fully ranges from 8.3 to 61.2 words, values that can be converted into time by assuming a reading speed, giving the range 2.6∼19.5 s for fast-reading and 5.3∼30.1 s for the average reader. Since a sentence conveys meaning, the surface features we have found might be a starting point to arrive at an information theory that includes meaning. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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27 pages, 1129 KiB  
Article
Integrated Attack Tree in Residual Risk Management Framework
by Ahmed Nawaz Khan, Jeremy Bryans, Giedre Sabaliauskaite and Hesamaldin Jadidbonab
Information 2023, 14(12), 639; https://doi.org/10.3390/info14120639 - 29 Nov 2023
Viewed by 4113
Abstract
Safety-critical cyber-physical systems (CPSs), such as high-tech cars having cyber capabilities, are highly interconnected. Automotive manufacturers are concerned about cyber attacks on vehicles that can lead to catastrophic consequences. There is a need for a new risk management approach to address and investigate [...] Read more.
Safety-critical cyber-physical systems (CPSs), such as high-tech cars having cyber capabilities, are highly interconnected. Automotive manufacturers are concerned about cyber attacks on vehicles that can lead to catastrophic consequences. There is a need for a new risk management approach to address and investigate cybersecurity risks. Risk management in the automotive domain is challenging due to technological improvements and advances every year. The current standard for automotive security is ISO/SAE 21434, which discusses a framework that includes threats, associated risks, and risk treatment options such as risk reduction by applying appropriate defences. This paper presents a residual cybersecurity risk management framework aligned with the framework presented in ISO/SAE 21434. A methodology is proposed to develop an integrated attack tree that considers multiple sub-systems within the CPS. Integrating attack trees in this way will help the analyst to take a broad perspective of system security. Our previous approach utilises a flow graph to calculate the residual risk to a system before and after applying defences. This paper is an extension of our initial work. It defines the steps for applying the proposed framework and using adaptive cruise control (ACC) and adaptive light control (ALC) to illustrate the applicability of our work. This work is evaluated by comparing it with the requirements of the risk management framework discussed in the literature. Currently, our methodology satisfies more than 75% of their requirements. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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24 pages, 1880 KiB  
Article
KVMod—A Novel Approach to Design Key-Value NoSQL Databases
by Ahmed Dourhri, Mohamed Hanine and Hassan Ouahmane
Information 2023, 14(10), 563; https://doi.org/10.3390/info14100563 - 12 Oct 2023
Viewed by 1773
Abstract
The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to [...] Read more.
The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage the rapidly growing quantity of data and variety of data structures, a new class of database management systems named NoSQL was required to satisfy the new requirements. Although NoSQL databases are generally schema-less, significant research has been conducted on their design. A literature review presented in this paper lets us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families that has received too little attention, especially in terms of its design methodology. Most studies have focused on the other families, like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers with a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum number of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and, thus, the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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26 pages, 2354 KiB  
Article
Effects of Generative Chatbots in Higher Education
by Galina Ilieva, Tania Yankova, Stanislava Klisarova-Belcheva, Angel Dimitrov, Marin Bratkov and Delian Angelov
Information 2023, 14(9), 492; https://doi.org/10.3390/info14090492 - 07 Sep 2023
Cited by 4 | Viewed by 7563
Abstract
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates [...] Read more.
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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17 pages, 2331 KiB  
Article
Developing a Serious Game for Rail Services: Improving Passenger Information During Disruption (PIDD)
by Ben Clegg, Richard Orme and Panagiotis Petridis
Information 2023, 14(8), 464; https://doi.org/10.3390/info14080464 - 17 Aug 2023
Viewed by 1054
Abstract
Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers [...] Read more.
Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers usually takes the form of practice reviews and ‘on-the-job’ learning, while academic education majors on theoretical approaches and classroom-based teaching. This paper reports on a novel industry-funded project that has developed a serious game (the ‘Rail Disruption Game’) that combines theory and practice to better manage PIDD for frontline staff in a UK train operating company (TOC). It defines challenges and the development method for the Rail Disruption Game; it also incorporates developer and user feedback. This paper provides insight into how to design, make and deploy a serious game as part of a gamified management process. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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14 pages, 609 KiB  
Article
InterviewBot: Real-Time End-to-End Dialogue System for Interviewing Students for College Admission
by Zihao Wang, Nathan Keyes, Terry Crawford and Jinho D. Choi
Information 2023, 14(8), 460; https://doi.org/10.3390/info14080460 - 15 Aug 2023
Viewed by 1080
Abstract
We present the InterviewBot, which dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 min hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges to assess their academic and cultural readiness. To build a [...] Read more.
We present the InterviewBot, which dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 min hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges to assess their academic and cultural readiness. To build a neural-based end-to-end dialogue model, 7361 audio recordings of human-to-human interviews are automatically transcribed, where 440 are manually corrected for finetuning and evaluation. To overcome the input/output size limit of a transformer-based encoder–decoder model, two new methods are proposed, context attention and topic storing, allowing the model to make relevant and consistent interactions. Our final model is tested both statistically by comparing its responses to the interview data and dynamically by inviting professional interviewers and various students to interact with it in real-time, finding it highly satisfactory in fluency and context awareness. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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23 pages, 2319 KiB  
Article
Unveiling Key Themes and Establishing a Hierarchical Taxonomy of Disaster-Related Tweets: A Text Mining Approach for Enhanced Emergency Management Planning
by James Durham, Sudipta Chowdhury and Ammar Alzarrad
Information 2023, 14(7), 385; https://doi.org/10.3390/info14070385 - 07 Jul 2023
Viewed by 1571
Abstract
Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and developing a taxonomy to effectively categorize and classify the diverse [...] Read more.
Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and developing a taxonomy to effectively categorize and classify the diverse range of topics discussed. First, the existing literature on social media analysis in disaster management is explored, highlighting the limitations and gaps in current methodologies. Second, a dataset comprising real-time social media posts related to various disasters is collected and preprocessed to ensure data quality and reliability. Third, three well-established topic modeling techniques, namely Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), are employed to extract and analyze the latent topics and themes present in the social media data. The contributions of this research lie in the development of a taxonomy that effectively categorizes and classifies disaster-related social media data, the identification of key latent topics and themes, and the extraction of valuable insights to support and enhance emergency management efforts. Overall, the findings of this research have the potential to transform the way emergency management and response are conducted by harnessing the power of social media data. By incorporating these insights into decision-making processes, emergency managers can make more informed and strategic choices, resulting in more efficient and effective emergency response strategies. This, in turn, leads to improved outcomes, better utilization of resources, and ultimately, the ability to save lives and mitigate the impacts of disasters. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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20 pages, 7807 KiB  
Article
INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance
by Evianita Dewi Fajrianti, Nobuo Funabiki, Sritrusta Sukaridhoto, Yohanes Yohanie Fridelin Panduman, Kong Dezheng, Fang Shihao and Anak Agung Surya Pradhana
Information 2023, 14(7), 359; https://doi.org/10.3390/info14070359 - 24 Jun 2023
Cited by 6 | Viewed by 2948
Abstract
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and [...] Read more.
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between 60∘ and 100∘ achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with 90∘ to 100∘ tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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26 pages, 1220 KiB  
Article
Matrices Based on Descriptors for Analyzing the Interactions between Agents and Humans
by Emmanuel Adam, Martial Razakatiana, René Mandiau and Christophe Kolski
Information 2023, 14(6), 313; https://doi.org/10.3390/info14060313 - 29 May 2023
Cited by 1 | Viewed by 1066
Abstract
The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It [...] Read more.
The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (e.g., criticality), but also human characteristics (e.g., workload and experience level) for the intervention (or not) of agents, to help the latter. Game theory is a well-known approach to studying such social interactions between different participants. Existing works on the construction of game matrices required different ad hoc descriptors, depending on the application studied. Moreover, they generally focused on the interactions between agents, without considering human beings in the analysis. We show that these descriptors can be classified into two categories, related to their effect on the interactions. The set of descriptors to use is thus based on an explicit combination of all interactions between agents and humans (a weighted sum of 2-player matrices). We propose a general model for the construction of game matrices based on any number of participants and descriptors. It is then possible to determine using Nash equilibria whether agents decide (or not) to intervene during the tasks concerned. The model is also evaluated through the determination of the gains obtained by the different participants. Finally, we illustrate and validate the proposed model using a typical scenario (involving two agents and two humans), while describing the corresponding equilibria. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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16 pages, 764 KiB  
Article
A Web-Based Docker Image Assistant Generation Tool for User-PC Computing System
by Lynn Htet Aung, Nobuo Funabiki, Soe Thandar Aung, Xudong Zhou, Xu Xiang and Wen-Chung Kao
Information 2023, 14(6), 300; https://doi.org/10.3390/info14060300 - 23 May 2023
Viewed by 1808
Abstract
Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle [...] Read more.
Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle every necessary software as one image file. Unfortunately, the Docker file/image are manually generated through multiple steps by a user, which can be the bottleneck. In this paper, we present a web-based Docker image assistant generation (DIAG) tool in the UPC system to assist or reduce these process steps. It adopts Angular JavaScript for offering user interfaces, PHP Laravel for handling logic using RestAPI, MySQL database for storing data, and Shell scripting for speedily running the whole program. In addition, the worker-side code modification function is implemented so that a user can modify the source code of the running job and update the Docker image at a worker to speed up them. For evaluations, we collected 30 Docker files and 10 OpenFOAM jobs through reverse processing from Docker images in Github and generated the Docker images using the tool. Moreover, we modified source codes for network simulations and generated the Docker images in a worker five times. The results confirmed the validity of the proposal. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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22 pages, 2608 KiB  
Article
Simulated Autonomous Driving Using Reinforcement Learning: A Comparative Study on Unity’s ML-Agents Framework
by Yusef Savid, Reza Mahmoudi, Rytis Maskeliūnas and Robertas Damaševičius
Information 2023, 14(5), 290; https://doi.org/10.3390/info14050290 - 14 May 2023
Cited by 3 | Viewed by 3813
Abstract
Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field [...] Read more.
Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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14 pages, 2169 KiB  
Article
Hemodynamic and Electrophysiological Biomarkers of Interpersonal Tuning during Interoceptive Synchronization
by Michela Balconi and Laura Angioletti
Information 2023, 14(5), 289; https://doi.org/10.3390/info14050289 - 13 May 2023
Cited by 1 | Viewed by 1436
Abstract
This research explored the influence of interoception and social frame on the coherence of inter-brain electrophysiological (EEG) and hemodynamic (collected by functional Near Infrared Spectroscopy, fNIRS) functional connectivity during a motor synchronization task. Fourteen dyads executed a motor synchronization task with the presence [...] Read more.
This research explored the influence of interoception and social frame on the coherence of inter-brain electrophysiological (EEG) and hemodynamic (collected by functional Near Infrared Spectroscopy, fNIRS) functional connectivity during a motor synchronization task. Fourteen dyads executed a motor synchronization task with the presence and absence of interoceptive focus. Moreover, the motor task was socially or not-socially framed by enhancing the shared intentionality. During the experiment, delta, theta, alpha, and beta frequency bands, and oxygenated and de-oxygenated hemoglobin (O2Hb and HHb) were collected through an EEG-fNIRS hyperscanning paradigm. Inter-brain coherence indices were computed for the two neurophysiological signals and then they were correlated to explore the reciprocal coherence of the functional connectivity EEG-fNIRS in the dyads. Findings showed significant higher correlational values between delta and O2Hb, theta and O2Hb, and alpha and O2Hb for the left hemisphere in the focus compared to the no focus condition and to the right hemisphere (both during focus and no focus condition). Additionally, greater correlational values between delta and O2Hb, and theta and O2Hb were observed in the left hemisphere for the focus condition when the task was socially compared to non-socially framed. This study showed that the focus on the breath and shared intentionality activate coherently the same left frontal areas in dyads performing a joint motor task. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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30 pages, 1272 KiB  
Article
Delay Indices for Train Punctuality
by Enrico Denti and Luca Burroni
Information 2023, 14(5), 269; https://doi.org/10.3390/info14050269 - 01 May 2023
Cited by 1 | Viewed by 2396
Abstract
Indicators of expected quality of service in public contracts are often based on some kind of “punctuality”, usually defined in terms of the percentage of trains arriving at the final destination (and/or at intermediate significant stops) within a given delay. Passengers, however, tend [...] Read more.
Indicators of expected quality of service in public contracts are often based on some kind of “punctuality”, usually defined in terms of the percentage of trains arriving at the final destination (and/or at intermediate significant stops) within a given delay. Passengers, however, tend to use the word “punctuality” with a more general meaning, mostly as a synonym for expected delay at their own destination, and especially in case of commuters are much less tolerant of even smaller delays than train operators would normally allow. In particular, measuring the delay only at the final destination is perceived as largely inadequate, leading to underestimation of the actual percentage of late trains, and in turn undermining passengers’ trust in official performance statistics. In this paper, we take the passengers’ perspective, introducing a family of delay indices called D-indices aimed at capturing the overall performance of a train “as a whole”, taking into account both the delays at the sampling points and the mutual location and order of such sampling points. In this paper, all indicators have the physical dimension of time in order to be easily replaceable to other delay measures. We first present typical approaches and definitions of punctuality in the literature, then introduce D-indices while exploring their features, pros and cons, and relevant properties. We validate and discuss our approach by comparing this model with existing approaches both theoretically and by comparison with selected datasets consisting of about one hundred trains transcribed over the last three years. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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24 pages, 4949 KiB  
Article
Modeling Chronic Pain Experiences from Online Reports Using the Reddit Reports of Chronic Pain Dataset
by Diogo A. P. Nunes, Joana Ferreira-Gomes, Fani Neto and David Martins de Matos
Information 2023, 14(4), 237; https://doi.org/10.3390/info14040237 - 12 Apr 2023
Viewed by 2026
Abstract
Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of [...] Read more.
Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of subreddits related to chronic pain, identify the main concerns discussed in each subreddit, model each subreddit according to their main concerns, and compare subreddit models. The RRCP dataset comprises 86,537 submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit was found to have various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Our analysis details each of these concerns and their (dis)similarity relations. Although limited by the intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent, chronic pain experiences. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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18 pages, 6347 KiB  
Article
Probabilistic Forecasting of Residential Energy Consumption Based on SWT-QRTCN-ADSC-NLSTM Model
by Ning Jin, Linlin Song, Gabriel Jing Huang and Ke Yan
Information 2023, 14(4), 231; https://doi.org/10.3390/info14040231 - 08 Apr 2023
Cited by 1 | Viewed by 1528
Abstract
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of [...] Read more.
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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25 pages, 3692 KiB  
Article
SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition
by Benjamin Jahić, Nicolas Guelfi and Benoît Ries
Information 2023, 14(4), 213; https://doi.org/10.3390/info14040213 - 01 Apr 2023
Cited by 3 | Viewed by 1608
Abstract
Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and [...] Read more.
Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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20 pages, 6151 KiB  
Article
Architecture-Oriented Agent-Based Simulations and Machine Learning Solution: The Case of Tsunami Emergency Analysis for Local Decision Makers
by Pavel Čech, Martin Mattoš, Viera Anderková, František Babič, Bilal Naji Alhasnawi, Vladimír Bureš, Milan Kořínek, Kamila Štekerová, Martina Husáková, Marek Zanker, Sunanda Manneela and Ioanna Triantafyllou
Information 2023, 14(3), 172; https://doi.org/10.3390/info14030172 - 08 Mar 2023
Viewed by 1731
Abstract
Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The [...] Read more.
Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The lack of detail, unification and standardisation in information processing and decision support hampers wider implementation of reusable information technology solutions among local authorities and officials. In this paper, the architecture of a tsunami emergency solution is introduced. The aim of the research is to present a tsunami emergency solution for local authorities and officials responsible for preparing tsunami response and evacuation plans. The solution is based on a combination of machine learning techniques and agent-based modelling, enabling analysis of both real and simulated datasets. The solution is designed and developed based on the principles of enterprise architecture development. The data exploration follows the practices for data mining and big data analyses. The architecture of the solution is depicted using the standardised notation and includes components that can be exploited by responsible local authorities to test various tsunami impact scenarios and prepare plans for appropriate response measures. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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22 pages, 14225 KiB  
Article
A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
by Emanuel Vieira, João Almeida, Joaquim Ferreira, Tiago Dias, Ana Vieira Silva and Lara Moura
Information 2023, 14(3), 153; https://doi.org/10.3390/info14030153 - 01 Mar 2023
Cited by 3 | Viewed by 2330
Abstract
Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the [...] Read more.
Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the cooperative perception of vehicles on the road, leading to increased road safety and traffic efficiency. Moreover, the central C-ITS system responsible for overseeing the road traffic and infrastructure, such as the RSUs, needs an efficient way of collecting and disseminating important information to road users. Warnings of accidents or other dangers, and other types of vehicular services such as Electronic Toll Collection (ETC), are examples of the types of information that the central C-ITS system is responsible for disseminating. To remedy these issues, we present the design of an implemented roadside and cloud architecture for the support of C-ITS services. With the main objectives of managing Vehicle-to-Everything (V2X) communication units and network messages of a public authority or motorway operator acting as a central C-ITS system, the proposed architecture was developed for different mobility testbeds in Portugal, under the scope of the STEROID research project and the pan-European Connected Roads (C-Roads) initiative. RSUs, equipped with ETSI ITS-G5 communications, are deployed with a cellular link or fiber optics connection for remote control and configuration. These are connected to a cloud Message Queuing Telemetry Transport (MQTT) broker where communication is based on a geographical tiling scheme, which allows the selection of the appropriate coverage areas for the dissemination of C-ITS messages. The architecture is deployed in the field, on several Portuguese motorways, where road traffic and infrastructure are monitored through a C-ITS platform with visualization and event reporting capabilities. The provided architecture is independent of the underlying communication technology and can be easily adapted in the future to support Cellular-V2X (PC5 interface) or 5G RSUs. Performance results of the deployed architecture are provided. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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10 pages, 1180 KiB  
Article
Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning
by Jiyan Cai, Libing Wu, Dan Wu, Jianxin Li and Xianfeng Wu
Information 2023, 14(3), 148; https://doi.org/10.3390/info14030148 - 24 Feb 2023
Cited by 2 | Viewed by 1519
Abstract
Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large [...] Read more.
Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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37 pages, 1682 KiB  
Article
Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
by Nikzad Chizari, Keywan Tajfar and María N. Moreno-García
Information 2023, 14(2), 131; https://doi.org/10.3390/info14020131 - 17 Feb 2023
Cited by 2 | Viewed by 2475
Abstract
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such [...] Read more.
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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36 pages, 3032 KiB  
Article
A Thorough Reproducibility Study on Sentiment Classification: Methodology, Experimental Setting, Results
by Giorgio Maria Di Nunzio and Riccardo Minzoni
Information 2023, 14(2), 76; https://doi.org/10.3390/info14020076 - 28 Jan 2023
Cited by 1 | Viewed by 1497
Abstract
A survey published by Nature in 2016 revealed that more than 70% of researchers failed in their attempt to reproduce another researcher’s experiments, and over 50% failed to reproduce one of their own experiments; a state of affairs that has been termed the [...] Read more.
A survey published by Nature in 2016 revealed that more than 70% of researchers failed in their attempt to reproduce another researcher’s experiments, and over 50% failed to reproduce one of their own experiments; a state of affairs that has been termed the ‘reproducibility crisis’ in science. The purpose of this work is to contribute to the field by presenting a reproducibility study of a Natural Language Processing paper about “Language Representation Models for Fine-Grained Sentiment Classification”. A thorough analysis of the methodology, experimental setting, and experimental results are presented, leading to a discussion of the issues and the necessary steps involved in this kind of study. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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15 pages, 444 KiB  
Article
NeuralMinimizer: A Novel Method for Global Optimization
by Ioannis G. Tsoulos, Alexandros Tzallas, Evangelos Karvounis and Dimitrios Tsalikakis
Information 2023, 14(2), 66; https://doi.org/10.3390/info14020066 - 25 Jan 2023
Cited by 1 | Viewed by 1528
Abstract
The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function [...] Read more.
The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function using only a few real samples from it. These samples construct the approach using a machine learning model. Next, the required sampling is performed by the approximation function. Furthermore, the approach is improved on each sample by using found local minima as samples for the training set of the machine learning model. In addition, as a termination criterion, the proposed technique uses a widely used criterion from the relevant literature which in fact evaluates it after each execution of the local minimization. The proposed technique was applied to a number of well-known problems from the relevant literature, and the comparative results with respect to modern global minimization techniques are shown to be extremely promising. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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12 pages, 982 KiB  
Article
Improving the Adversarial Robustness of Neural ODE Image Classifiers by Tuning the Tolerance Parameter
by Fabio Carrara, Roberto Caldelli, Fabrizio Falchi and Giuseppe Amato
Information 2022, 13(12), 555; https://doi.org/10.3390/info13120555 - 26 Nov 2022
Viewed by 1496
Abstract
The adoption of deep learning-based solutions practically pervades all the diverse areas of our everyday life, showing improved performances with respect to other classical systems. Since many applications deal with sensible data and procedures, a strong demand to know the actual reliability of [...] Read more.
The adoption of deep learning-based solutions practically pervades all the diverse areas of our everyday life, showing improved performances with respect to other classical systems. Since many applications deal with sensible data and procedures, a strong demand to know the actual reliability of such technologies is always present. This work analyzes the robustness characteristics of a specific kind of deep neural network, the neural ordinary differential equations (N-ODE) network. They seem very interesting for their effectiveness and a peculiar property based on a test-time tunable parameter that permits obtaining a trade-off between accuracy and efficiency. In addition, adjusting such a tolerance parameter grants robustness against adversarial attacks. Notably, it is worth highlighting how decoupling the values of such a tolerance between training and test time can strongly reduce the attack success rate. On this basis, we show how such tolerance can be adopted, during the prediction phase, to improve the robustness of N-ODE to adversarial attacks. In particular, we demonstrate how we can exploit this property to construct an effective detection strategy and increase the chances of identifying adversarial examples in a non-zero knowledge attack scenario. Our experimental evaluation involved two standard image classification benchmarks. This showed that the proposed detection technique provides high rejection of adversarial examples while maintaining most of the pristine samples. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Review

Jump to: Research

31 pages, 1690 KiB  
Review
A Review of the Consent Management Literature
by Christian Bonnici West and Simon Grima
Information 2024, 15(2), 79; https://doi.org/10.3390/info15020079 - 31 Jan 2024
Viewed by 995
Abstract
The richness and complexity of consent present challenges to those aiming to make related contributions to computer information systems (CIS). This paper aims to support consent-related research in CIS by simplifying the understanding of existing literature and facilitating the framing of future consent [...] Read more.
The richness and complexity of consent present challenges to those aiming to make related contributions to computer information systems (CIS). This paper aims to support consent-related research in CIS by simplifying the understanding of existing literature and facilitating the framing of future consent management research. Firstly, it outlines existing consent management research and shows how it relates to the literature in law and ethics. Secondly, it presents some fundamental explanations and definitions that must be considered for further contributions to the consent management literature. Thirdly, it identifies five types of consent-related stances often taken in the consent management literature and explains each in some detail. Fourth, it explains one of the identified types of stances (i.e., the disciplinary stance) by expanding on the links between consent as a legal construct and its ethical counterpart. Fifth, considering another of the identified types of stances (i.e., the theoretical stances normally adopted in the consent management literature), the paper presents the key requirements for legally and ethically effective consent management based on three prominent theories. Sixth, it presents the identified types of stances in a conceptual model, contending that the model is novel, relevant, understandable, and useful. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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