Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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23 pages, 1481 KiB  
Article
Enabling Blockchain with IoMT Devices for Healthcare
by Jameel Almalki, Waleed Al Shehri, Rashid Mehmood, Khalid Alsaif, Saeed M. Alshahrani, Najlaa Jannah and Nayyar Ahmed Khan
Information 2022, 13(10), 448; https://doi.org/10.3390/info13100448 - 25 Sep 2022
Cited by 12 | Viewed by 3340
Abstract
Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem [...] Read more.
Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes. Full article
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14 pages, 3119 KiB  
Article
Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions
by Emmanuel A. Adeniyi, Peace Busola Falola, Mashael S. Maashi, Mohammed Aljebreen and Salil Bharany
Information 2022, 13(10), 442; https://doi.org/10.3390/info13100442 - 20 Sep 2022
Cited by 10 | Viewed by 3051
Abstract
With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and [...] Read more.
With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process. Full article
(This article belongs to the Special Issue Secure and Trustworthy Cyber–Physical Systems)
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17 pages, 1739 KiB  
Article
Local Multi-Head Channel Self-Attention for Facial Expression Recognition
by Roberto Pecoraro, Valerio Basile and Viviana Bono
Information 2022, 13(9), 419; https://doi.org/10.3390/info13090419 - 06 Sep 2022
Cited by 30 | Viewed by 2416
Abstract
Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be [...] Read more.
Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be easily integrated into virtually every convolutional neural network, and that is specifically designed for computer vision, with a specific focus on facial expression recognition. LHC is based on two main ideas: first, we think that in computer vision, the best way to leverage the self-attention paradigm is the channel-wise application instead of the more well explored spatial attention. Secondly, a local approach has the potential to better overcome the limitations of convolution than global attention, at least in those scenarios where images have a constant general structure, as in facial expression recognition. LHC-Net achieves a new state-of-the-art in the FER2013 dataset, with a significantly lower complexity and impact on the “host” architecture in terms of computational cost when compared with the previous state-of-the-art. Full article
(This article belongs to the Section Artificial Intelligence)
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20 pages, 4365 KiB  
Systematic Review
A Comparative Study of the ADDIE Instructional Design Model in Distance Education
by Adamantia G. Spatioti, Ioannis Kazanidis and Jenny Pange
Information 2022, 13(9), 402; https://doi.org/10.3390/info13090402 - 23 Aug 2022
Cited by 21 | Viewed by 18227
Abstract
Distance education is now a reality introducing a “specific methodology of flexible and interactive multiform learning”. Due to its characteristics, different instructional design models apply to distance education as guidelines of the design thinking process pursuing specific learning outcomes. This study [...] Read more.
Distance education is now a reality introducing a “specific methodology of flexible and interactive multiform learning”. Due to its characteristics, different instructional design models apply to distance education as guidelines of the design thinking process pursuing specific learning outcomes. This study refers to the investigation of good teaching practices and approaches in relation to the ADDIE model in distance online environments. The purpose of this paper is to investigate both the effectiveness of the ADDIE model in distance education and its contribution to the online teaching process. Meta-analysis is chosen as the research methodology. Specifically, we export a total of 58 articles referring to the ADDIE model. From these, we find that only 23 articles are appropriate for the meta-analysis. According to the results of this study, we observe that the ADDIE model applies to meet different teaching requirements in all online educational environments. In this study, we observe that good practices of teaching are the multimedia presentation, feedback, variety of interactive exercises or activities, combined learning strategy (individualized and collaborative), and role of educators. Then, an asynchronous approach was preferred in distance education. Finally, the ADDIE model is considered as a valuable source of additional information by providing good teaching practices. Full article
(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
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19 pages, 2045 KiB  
Review
A Review of Knowledge Graph Completion
by Mohamad Zamini, Hassan Reza and Minou Rabiei
Information 2022, 13(8), 396; https://doi.org/10.3390/info13080396 - 21 Aug 2022
Cited by 28 | Viewed by 5807
Abstract
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs [...] Read more.
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. Full article
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14 pages, 4173 KiB  
Article
Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking
by Alessandro Renda, Pietro Ducange, Francesco Marcelloni, Dario Sabella, Miltiadis C. Filippou, Giovanni Nardini, Giovanni Stea, Antonio Virdis, Davide Micheli, Damiano Rapone and Leonardo Gomes Baltar
Information 2022, 13(8), 395; https://doi.org/10.3390/info13080395 - 20 Aug 2022
Cited by 24 | Viewed by 4579
Abstract
This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has [...] Read more.
This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications. Full article
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence)
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15 pages, 1457 KiB  
Article
Sustainable Mobility as a Service: Demand Analysis and Case Studies
by Giuseppe Musolino
Information 2022, 13(8), 376; https://doi.org/10.3390/info13080376 - 05 Aug 2022
Cited by 14 | Viewed by 2462
Abstract
Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are [...] Read more.
Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are the central element of the transport service. The objective of this paper is to build an updated state-of-the-art of the main disaggregated and aggregated variables connected to travel demand in presence of MaaS. According to the above objective, this paper deals with methods and case studies to analyze passengers’ behaviour in the presence of MaaS. The methods described rely on the Transportation System Models (TSMs), in particular with the travel demand modelling component. The travel demand may be estimated by means of disaggregated, or sample, surveys (e.g., individual choices) and of aggregate surveys (e.g., characteristics of the area, traffic flows). The surveys are generally supported by Information Communication System (ICT) tools, such as: smartphones; smartcards; Global Position Systems (GPS); points of interest. The analysis of case studies allows to aggregate the existing scientific literature according to some criteria: the choice dimension of users (e.g., mode, bundle and path, or a combination of them); the characteristics of the survey (e.g., revealed preferences or stated preferences); the presence of behavioural theoretical background and of calibrated choice model(s). Full article
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25 pages, 1840 KiB  
Article
Sustainable Mobility as a Service: Dynamic Models for Agenda 2030 Policies
by Francesco Russo
Information 2022, 13(8), 355; https://doi.org/10.3390/info13080355 - 25 Jul 2022
Cited by 16 | Viewed by 2403
Abstract
Growth trends in passenger transport demand and gross domestic product have so far been similar. The increase in mobility in one area is connected with the increase in GDP in the same area. This increase is representative of the economic and social development [...] Read more.
Growth trends in passenger transport demand and gross domestic product have so far been similar. The increase in mobility in one area is connected with the increase in GDP in the same area. This increase is representative of the economic and social development of the area. At the same time, the increase in mobility produces one of the most negative environmental impacts, mainly determined by the growth of mobility of private cars. International attention is given to the possibilities of increasing mobility and, therefore, social and economic development without increasing environmental impacts. One of the most promising fields is that of MaaS: Mobility as a Service. MaaS arises from the interaction of new user behavioral models (demand) and new decision-making models on services (supply). Advanced interaction arises from the potentialities allowed by emerging ICT technologies. There is a delay in the advancement of transport system models that consider the updating of utility and choice for the user by means of updated information. The paper introduces sustainability as defined by Agenda 2030 with respect to urban passenger transport, then examines the role of ICT in the development of MaaS formalizing a dynamic model of demand–supply interaction explicating ICT. Finally, the advanced Sustainable MaaS, defined SMaaS, is analyzed, evidencing the contribution to achieving the goals of Agenda 2030. Full article
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19 pages, 2101 KiB  
Article
Sustainable Mobility as a Service: Supply Analysis and Test Cases
by Corrado Rindone
Information 2022, 13(7), 351; https://doi.org/10.3390/info13070351 - 21 Jul 2022
Cited by 21 | Viewed by 2952
Abstract
Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as [...] Read more.
Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as a Service (MaaS) offers an alternative to unsustainable mobility, often based on private car use. From the point of view of sustainable mobility, the MaaS paradigm implies greater insights into the transport system and its components (supply, demand, and reciprocal interactions). This paper proposes an approach to the transport system aimed at overcoming the current barriers to the implementation of the paradigm. The focus is on the implications for the transport supply subsystem. The investigation method is based on the analysis of the main components of such subsystem (governance, immaterial, material, equipment) and its role in the entire transport system. Starting with the first experiences of Finnish cities, the paper investigates some real case studies, which are experimenting with MaaS, to find common and uncommon elements. From the analyses, it emerges that the scientific literature and real experiences mainly focus on the immaterial components alone. To address the challenges related to sustainable mobility, this paper underlines the need to consider all components within a transport system approach. The findings of the paper are useful in several contexts. In the context of research, the paper offers an analysis of the transport supply system from the point of view of the MaaS paradigm. In the real context, the paper offers further useful insights for operators and decision-makers who intend to increase the knowledge and skills necessary to face challenges related to the introduction of MaaS. Full article
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15 pages, 2061 KiB  
Article
Sustainable Mobility as a Service: Framework and Transport System Models
by Antonino Vitetta
Information 2022, 13(7), 346; https://doi.org/10.3390/info13070346 - 16 Jul 2022
Cited by 21 | Viewed by 3390
Abstract
Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the [...] Read more.
Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the sustainability objectives and goals to be achieved with particular reference to the consolidated methodologies adopted in the transport systems engineering for design, management, and monitoring of transport services; it is defined as Sustainable MaaS (S-MaaS). This paper considers the technological and communication platform essential and assumed to be a given considering that it has been proposed in many papers and it has been tested in some areas together with MaaS. Starting from the MaaS platform, the additional components and models necessary for the implementation of an S-MaaS are analyses in relation to: a Decision Support System (DSS) that supports MaaS public administrations and MaaS companies for the design of the service and demand management; a system for the evaluation of intervention policies; and also considers smart planning for a priori and a posteriori evaluation of sustainability objectives and targets. Full article
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12 pages, 1826 KiB  
Article
Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT
by Omar Abdulkareem Mahmood, Ali R. Abdellah, Ammar Muthanna and Andrey Koucheryavy
Information 2022, 13(7), 328; https://doi.org/10.3390/info13070328 - 07 Jul 2022
Cited by 15 | Viewed by 2535
Abstract
Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their [...] Read more.
Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks. Full article
(This article belongs to the Special Issue Advances in Wireless Communications Systems)
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16 pages, 3021 KiB  
Article
An Intrusion Detection Method for Industrial Control System Based on Machine Learning
by Yixin Cao, Lei Zhang, Xiaosong Zhao, Kai Jin and Ziyi Chen
Information 2022, 13(7), 322; https://doi.org/10.3390/info13070322 - 03 Jul 2022
Cited by 6 | Viewed by 2886
Abstract
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, [...] Read more.
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, which means the traditional IDS cannot cope with unknown attacks. Additionally, most IDS do not consider the imbalanced nature of ICS datasets, thus suffering from low accuracy and high False Positive Rates when being put to use. In this paper, we propose the NCO–double-layer DIFF_RF–OPFYTHON intrusion detection method for ICS, which consists of NCO modules, double-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the double-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Then, the known attacks will be classified into specific attacks by the OPFYTHON module according to the feature of attack traffic. Finally, we use the NCO module to improve the model input and enhance the accuracy of the model. The results show that the proposed method outperforms traditional intrusion detection methods, such as XGboost and SVM. The detection of unknown attacks is also considerable. The accuracy of the dataset used in this paper reaches 98.13%. The detection rates for unknown attacks and known attacks reach 98.21% and 95.1%, respectively. Moreover, the method we proposed has achieved suitable results on other public datasets. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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15 pages, 2863 KiB  
Article
Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems
by Hamid Keshmiri Neghab, Mohammad (Behdad) Jamshidi and Hamed Keshmiri Neghab
Information 2022, 13(7), 321; https://doi.org/10.3390/info13070321 - 01 Jul 2022
Cited by 27 | Viewed by 2721
Abstract
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber [...] Read more.
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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12 pages, 2876 KiB  
Review
Fourth Industrial Revolution between Knowledge Management and Digital Humanities
by Muhammad Anshari, Muhammad Syafrudin and Norma Latif Fitriyani
Information 2022, 13(6), 292; https://doi.org/10.3390/info13060292 - 08 Jun 2022
Cited by 20 | Viewed by 8952
Abstract
The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous [...] Read more.
The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous robots, additive manufacturing (3D Printing), and cloud computing for optimization purposes. However, the impact of 4IR has brought various changes to digital humanities, mainly in the occupations of people, but also in ethical compliance. It still requires the redefining of the roles of knowledge management (KM) as one of the tools to assist in organization growth, especially in negotiating tasks between machines and people in an organization. Knowledge management is crucial in the development of new digital skills that are governed by the ethical obligations that are necessary in the Fourth Industrial Revolution. The purpose of the study is to examine the role of KM strategies in responding to the emergence of 4IR, its impact on and challenges to the labor market, and employment. This paper also analyzes and further discusses how 4IR and employment issues are being viewed in the context of ethical dilemmas. Full article
(This article belongs to the Special Issue Knowledge Management and Digital Humanities)
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16 pages, 473 KiB  
Article
Multimodal Fake News Detection
by Isabel Segura-Bedmar and Santiago Alonso-Bartolome
Information 2022, 13(6), 284; https://doi.org/10.3390/info13060284 - 02 Jun 2022
Cited by 27 | Viewed by 6014
Abstract
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools [...] Read more.
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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12 pages, 2486 KiB  
Article
Efficient Edge-AI Application Deployment for FPGAs
by Stavros Kalapothas, Georgios Flamis and Paris Kitsos
Information 2022, 13(6), 279; https://doi.org/10.3390/info13060279 - 28 May 2022
Cited by 12 | Viewed by 4631
Abstract
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks (DNN) architectures. FPGAs have gained their reputation due to the greater energy efficiency and high parallelism than microcontrollers (MCU) and [...] Read more.
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks (DNN) architectures. FPGAs have gained their reputation due to the greater energy efficiency and high parallelism than microcontrollers (MCU) and graphical processing units (GPU), while they are easier to develop and more reconfigurable than the Application Specific Integrated Circuit (ASIC). The development and building of AI applications on resource constraint devices such as FPGAs remains a challenge, however, due to the co-design approach, which requires a valuable expertise in low-level hardware design and in software development. This paper explores the efficacy and the dynamic deployment of hardware accelerated applications on the Kria KV260 development platform based on the Xilinx Kria K26 system-on-module (SoM), which includes a Zynq multiprocessor system-on-chip (MPSoC). The platform supports the Python-based PYNQ framework and maintains a high level of versatility with the support of custom bitstreams (overlays). The demonstration proved the reconfigurabibilty and the overall ease of implementation with low-footprint machine learning (ML) algorithms. Full article
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25 pages, 1964 KiB  
Review
A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology
by Roseline Oluwaseun Ogundokun, Sanjay Misra, Rytis Maskeliunas and Robertas Damasevicius
Information 2022, 13(5), 263; https://doi.org/10.3390/info13050263 - 23 May 2022
Cited by 22 | Viewed by 5353
Abstract
Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. [...] Read more.
Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. Full article
(This article belongs to the Special Issue Foundations and Challenges of Interpretable ML)
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16 pages, 905 KiB  
Article
A Study of Inbound Travelers Experience and Satisfaction at Quarantine Hotels in Indonesia during the COVID-19 Pandemic
by Narariya Dita Handani, Aura Lydia Riswanto and Hak-Seon Kim
Information 2022, 13(5), 254; https://doi.org/10.3390/info13050254 - 13 May 2022
Cited by 13 | Viewed by 2727
Abstract
The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being [...] Read more.
The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being relaxed, hotel satisfaction becomes a crucial factor in quarantine hotels. If guests have a positive experience while staying at these hotels, they are likely to return for a staycation or vacation in the near future. The study examined 4856 reviews from Google reviews on 15 quarantine hotels in Indonesia. Following word frequency calculations in a matrix, UCINET 6.0 is used to analyze the network centrality and perform CONCOR analysis. The CONCOR analysis categorizes the review data into five categories. As quantitative analysis was performed, exploratory factor analysis was grouped into six variables: tangible, assurance, frontline, accommodation, quarantine, and location. As a result, tangible, assurance, and frontline negatively impacted guest satisfaction. Furthermore, three other variables: accommodation, quarantine, location, which have a positive influence, will lead to increased trust from inbound travelers. For managerial implication, results allow managers of quarantine hotels in Indonesia to focus more on improving tangible, assurance, and frontline factors. Full article
(This article belongs to the Special Issue Data Analytics and Consumer Behavior)
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15 pages, 634 KiB  
Article
Decision-Making Model for Reinforcing Digital Transformation Strategies Based on Artificial Intelligence Technology
by Kyungtae Kim and Boyoung Kim
Information 2022, 13(5), 253; https://doi.org/10.3390/info13050253 - 13 May 2022
Cited by 13 | Viewed by 5631
Abstract
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study [...] Read more.
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study aims to define the factors affecting digital transformation strategies and present a decision-making model required for digital transformation strategies based on the definition. It also reviews previous AI technology and digital transformation strategies and draws influence factors. The research model drew four evaluation areas, such as subject, environment, resource, and mechanism, and 16 evaluation factors through the SERM model. After the factors were reviewed through the Delphi methods, a questionnaire survey was conducted targeting experts with over 10 years of work experience in the digital strategy field. The study results were produced by comparing the data’s importance using an Analytic Hierarchy Process (AHP) on each group. According to the analysis, the subject was the most critical factor, and the CEO (top management) was more vital than the core talent or technical development organization. The importance was shown in the order of resource, mechanism and environment, following subject. It was ascertained that there were differences of importance in industrial competition and market digitalization in the demander and provider groups. Full article
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28 pages, 3420 KiB  
Article
Enhancing Food Supply Chain Security through the Use of Blockchain and TinyML
by Vasileios Tsoukas, Anargyros Gkogkidis, Aikaterini Kampa, Georgios Spathoulas and Athanasios Kakarountas
Information 2022, 13(5), 213; https://doi.org/10.3390/info13050213 - 20 Apr 2022
Cited by 26 | Viewed by 6910
Abstract
Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in [...] Read more.
Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in food supply chains that extend beyond nations or even continent boundaries. Food supply networks are characterized by high complexity and a lack of openness. There is a critical requirement for applying novel techniques to verify and authenticate the origin, quality parameters, and transfer/storage details associated with food. This study portrays an end-to-end approach to enhance the security of the food supply chain and thus increase the trustfulness of the food industry. The system aims at increasing the transparency of food supply chain monitoring systems through securing all components that those consist of. A universal information monitoring scheme based on blockchain technology ensures the integrity of collected data, a self-sovereign identity approach for all supply chain actors ensures the minimization of single points of failure, and finally, a security mechanism, that is based on the use of TinyML’s nascent technology, is embedded in monitoring devices to mitigate a significant portion of malicious behavior from actors in the supply chain. Full article
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15 pages, 2590 KiB  
Article
Audio Storytelling Innovation in a Digital Age: The Case of Daily News Podcasts in Spain
by Leoz-Aizpuru Asier and Pedrero-Esteban Luis Miguel
Information 2022, 13(4), 204; https://doi.org/10.3390/info13040204 - 18 Apr 2022
Cited by 11 | Viewed by 3784
Abstract
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and [...] Read more.
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and more than 3000 million accumulated downloads. The unprecedented success of this audio format, that has emerged in a traditional newspaper, has helped to revamp radio news and has spread in various versions all over the world. This investigation analyses daily podcasts in Spain and, by means of a quantitative and qualitative study, identifies their main themes, narrative structures, and expressive contributions based on four illustrative experiences in this market: ‘Quién dice qué‘, ‘AM’, ‘El Mundo al día’, and ‘Un tema al día’. The results reveal the consolidation of two clearly defined models: a more conventional one based on radio bulletins and news reports; and another, more innovative model that replicates the anglophone formula that opts for depth, dissemination, and a conversational tone to redefine the canons of the audio news narrative and take advantage of the potential of audio as a new distribution channel for newspapers in the digital eco-system. Full article
(This article belongs to the Special Issue Advances in Interactive and Digital Media)
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26 pages, 609 KiB  
Review
Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
by Hussan Munir, Bahtijar Vogel and Andreas Jacobsson
Information 2022, 13(4), 203; https://doi.org/10.3390/info13040203 - 17 Apr 2022
Cited by 24 | Viewed by 9407
Abstract
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a [...] Read more.
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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11 pages, 1388 KiB  
Article
A LiDAR–Camera Fusion 3D Object Detection Algorithm
by Leyuan Liu, Jian He, Keyan Ren, Zhonghua Xiao and Yibin Hou
Information 2022, 13(4), 169; https://doi.org/10.3390/info13040169 - 26 Mar 2022
Cited by 14 | Viewed by 4172
Abstract
3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. [...] Read more.
3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. Secondly, an attention-based fusion sub-network is designed to fuse the features extracted by the 2D backbone and the features extracted from 3D LiDAR point clouds by PointNet++. Besides, the FuDNN, which uses the RPN and the refinement work of PointRCNN to obtain 3D box predictions, was tested on the public KITTI dataset. Experiments on the KITTI validation set show that the proposed FuDNN achieves AP values of 92.48, 82.90, and 80.51 at easy, moderate, and hard difficulty levels for car detection. The proposed FuDNN improves the performance of LiDAR–camera fusion 3D object detection in the car category of the public KITTI dataset. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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12 pages, 1491 KiB  
Article
Online Customer Reviews and Satisfaction with an Upscale Hotel: A Case Study of Atlantis, The Palm in Dubai
by Shengnan Wei and Hak-Seon Kim
Information 2022, 13(3), 150; https://doi.org/10.3390/info13030150 - 12 Mar 2022
Cited by 13 | Viewed by 3895
Abstract
The main purpose of this study is to explore the insights of customers’ reviews from the upscale hotel Atlantis, The Palm in the Dubai area. The data was collected from the SCTM 3.0 (smart crawling and text mining) platform developed by the Wellness [...] Read more.
The main purpose of this study is to explore the insights of customers’ reviews from the upscale hotel Atlantis, The Palm in the Dubai area. The data was collected from the SCTM 3.0 (smart crawling and text mining) platform developed by the Wellness & Tourism Big Data Institute at Kyungsung University. A total of 2051 online reviews were collected from the period from 29 October 2018 to 29 October 2021. The following steps were conducted by RStudio and UCINET 6.0 to analyze the collected data and to visualize the results. The results showed the top 50 keywords customers used in the reviews, such as ‘great’, ‘amazing’, or ‘service’. Exploratory factor analysis (EFA) and linear regression analysis were applied for an in-depth understanding of customer satisfaction. The analysis results demonstrated that the ‘value’ and ‘dining’ factors had a negative influence on overall customer satisfaction. These findings could provide managerial and marketing insights for upscale hotel managers when formulating and implementing strategies and tactics to improve customer satisfaction. Full article
(This article belongs to the Special Issue Data Analytics and Consumer Behavior)
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16 pages, 4879 KiB  
Article
Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate
by Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, Cheng-Chi Lee and Chun-Ta Li
Information 2022, 13(3), 141; https://doi.org/10.3390/info13030141 - 07 Mar 2022
Cited by 15 | Viewed by 3505
Abstract
Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the [...] Read more.
Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band. Full article
(This article belongs to the Special Issue Advances in Wireless Communications Systems)
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14 pages, 1941 KiB  
Article
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
by Giorgio De Magistris, Samuele Russo, Paolo Roma, Janusz T. Starczewski and Christian Napoli
Information 2022, 13(3), 137; https://doi.org/10.3390/info13030137 - 07 Mar 2022
Cited by 25 | Viewed by 4762
Abstract
Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social [...] Read more.
Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure. Full article
(This article belongs to the Special Issue Signal Processing Based on Convolutional Neural Network)
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16 pages, 487 KiB  
Article
Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges
by Oumaima Stitini, Soulaimane Kaloun and Omar Bencharef
Information 2022, 13(3), 128; https://doi.org/10.3390/info13030128 - 03 Mar 2022
Cited by 12 | Viewed by 6236
Abstract
In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like [...] Read more.
In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents. Full article
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19 pages, 348 KiB  
Article
ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide
by Adel Ben Youssef, Mounir Dahmani and Ludovic Ragni
Information 2022, 13(3), 129; https://doi.org/10.3390/info13030129 - 03 Mar 2022
Cited by 38 | Viewed by 46136
Abstract
Information and communication technologies (ICTs) are an integral part of our environment, and their uses vary across generations and among individuals. Today’s student population is made up of “digital natives” who have grown up under the ubiquitous influence of digital technologies, and for [...] Read more.
Information and communication technologies (ICTs) are an integral part of our environment, and their uses vary across generations and among individuals. Today’s student population is made up of “digital natives” who have grown up under the ubiquitous influence of digital technologies, and for whom the use of ICT is common and whose daily activities are structured around media use. The aim of this study is to examine the impact of ICT use and digital skills on students’ academic performance and to explore the digital divide in France. Data were collected through face-to-face questionnaires administered to 1323 students enrolled in three French universities. Principal component analysis, a non-hierarchical k-means clustering approach and multilevel ordered logistic regression were used for data analysis and provide four main findings: first, poor investment in ICT affects students’ results; second, the ICT training offered by universities has little impact on students’ results; third, student performance improves with the innovative and collaborative use of ICTs; fourth, the acquisition of digital skills increases students’ academic performance. The results show that the digital divide still exists, and this raises questions about the effectiveness of education policies in France. They suggest also that organizational change in universities is essential to enable an exploitation of ICT. Full article
(This article belongs to the Special Issue Beyond Digital Transformation: Digital Divides and Digital Dividends)
16 pages, 17025 KiB  
Article
Breast Histopathological Image Classification Method Based on Autoencoder and Siamese Framework
by Min Liu, Yu He, Minghu Wu and Chunyan Zeng
Information 2022, 13(3), 107; https://doi.org/10.3390/info13030107 - 24 Feb 2022
Cited by 17 | Viewed by 2783
Abstract
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopathological images, extracting features for breast cancer classification is difficult. [...] Read more.
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopathological images, extracting features for breast cancer classification is difficult. To address this problem, an improved autoencoder (AE) network using a Siamese framework that can learn the effective features from histopathological images for CAD breast cancer classification tasks was designed. First, the inputted image is processed at multiple scales using a Gaussian pyramid to obtain multi-scale features. Second, in the feature extraction stage, a Siamese framework is used to constrain the pre-trained AE so that the extracted features have smaller intra-class variance and larger inter-class variance. Experimental results show that the proposed method classification accuracy was as high as 97.8% on the BreakHis dataset. Compared with commonly used algorithms in breast cancer histopathological classification, this method has superior, faster performance. Full article
(This article belongs to the Topic Artificial Intelligence (AI) in Medical Imaging)
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27 pages, 3625 KiB  
Article
Navigation Data Anomaly Analysis and Detection
by Ahmed Amro, Aybars Oruc, Vasileios Gkioulos and Sokratis Katsikas
Information 2022, 13(3), 104; https://doi.org/10.3390/info13030104 - 23 Feb 2022
Cited by 10 | Viewed by 4186
Abstract
Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in [...] Read more.
Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Furthermore, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA-Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system. Full article
(This article belongs to the Special Issue Cyber-Security for the Maritime Industry)
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13 pages, 4205 KiB  
Article
A New Edge Computing Architecture for IoT and Multimedia Data Management
by Olivier Debauche, Saïd Mahmoudi and Adriano Guttadauria
Information 2022, 13(2), 89; https://doi.org/10.3390/info13020089 - 14 Feb 2022
Cited by 18 | Viewed by 6480
Abstract
The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we [...] Read more.
The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT). Full article
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39 pages, 794 KiB  
Review
A Survey on Text Classification Algorithms: From Text to Predictions
by Andrea Gasparetto, Matteo Marcuzzo, Alessandro Zangari and Andrea Albarelli
Information 2022, 13(2), 83; https://doi.org/10.3390/info13020083 - 11 Feb 2022
Cited by 47 | Viewed by 14102
Abstract
In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development [...] Read more.
In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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15 pages, 3025 KiB  
Article
Architecture of a Hybrid Video/Optical See-through Head-Mounted Display-Based Augmented Reality Surgical Navigation Platform
by Marina Carbone, Fabrizio Cutolo, Sara Condino, Laura Cercenelli, Renzo D’Amato, Giovanni Badiali and Vincenzo Ferrari
Information 2022, 13(2), 81; https://doi.org/10.3390/info13020081 - 08 Feb 2022
Cited by 16 | Viewed by 3592
Abstract
In the context of image-guided surgery, augmented reality (AR) represents a ground-breaking enticing improvement, mostly when paired with wearability in the case of open surgery. Commercially available AR head-mounted displays (HMDs), designed for general purposes, are increasingly used outside their indications to develop [...] Read more.
In the context of image-guided surgery, augmented reality (AR) represents a ground-breaking enticing improvement, mostly when paired with wearability in the case of open surgery. Commercially available AR head-mounted displays (HMDs), designed for general purposes, are increasingly used outside their indications to develop surgical guidance applications with the ambition to demonstrate the potential of AR in surgery. The applications proposed in the literature underline the hunger for AR-guidance in the surgical room together with the limitations that hinder commercial HMDs from being the answer to such a need. The medical domain demands specifically developed devices that address, together with ergonomics, the achievement of surgical accuracy objectives and compliance with medical device regulations. In the framework of an EU Horizon2020 project, a hybrid video and optical see-through augmented reality headset paired with a software architecture, both specifically designed to be seamlessly integrated into the surgical workflow, has been developed. In this paper, the overall architecture of the system is described. The developed AR HMD surgical navigation platform was positively tested on seven patients to aid the surgeon while performing Le Fort 1 osteotomy in cranio-maxillofacial surgery, demonstrating the value of the hybrid approach and the safety and usability of the navigation platform. Full article
(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)
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10 pages, 403 KiB  
Article
Performance Study on Extractive Text Summarization Using BERT Models
by Shehab Abdel-Salam and Ahmed Rafea
Information 2022, 13(2), 67; https://doi.org/10.3390/info13020067 - 28 Jan 2022
Cited by 33 | Viewed by 8632
Abstract
The task of summarization can be categorized into two methods, extractive and abstractive. Extractive summarization selects the salient sentences from the original document to form a summary while abstractive summarization interprets the original document and generates the summary in its own words. The [...] Read more.
The task of summarization can be categorized into two methods, extractive and abstractive. Extractive summarization selects the salient sentences from the original document to form a summary while abstractive summarization interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches in the literature, including statistical-, graph-, and deep learning-based approaches. Deep learning has achieved promising performances in comparison to the classical approaches, and with the advancement of different neural architectures such as the attention network (commonly known as the transformer), there are potential areas of improvement for the summarization task. The introduction of transformer architecture and its encoder model “BERT” produced an improved performance in downstream tasks in NLP. BERT is a bidirectional encoder representation from a transformer modeled as a stack of encoders. There are different sizes for BERT, such as BERT-base with 12 encoders and BERT-larger with 24 encoders, but we focus on the BERT-base for the purpose of this study. The objective of this paper is to produce a study on the performance of variants of BERT-based models on text summarization through a series of experiments, and propose “SqueezeBERTSum”, a trained summarization model fine-tuned with the SqueezeBERT encoder variant, which achieved competitive ROUGE scores retaining the BERTSum baseline model performance by 98%, with 49% fewer trainable parameters. Full article
(This article belongs to the Special Issue Novel Methods and Applications in Natural Language Processing)
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33 pages, 748 KiB  
Article
An Attribute-Based Approach toward a Secured Smart-Home IoT Access Control and a Comparison with a Role-Based Approach
by Safwa Ameer, James Benson and Ravi Sandhu
Information 2022, 13(2), 60; https://doi.org/10.3390/info13020060 - 25 Jan 2022
Cited by 17 | Viewed by 3643
Abstract
The area of smart homes is one of the most popular for deploying smart connected devices. One of the most vulnerable aspects of smart homes is access control. Recent advances in IoT have led to several access control models being developed or adapted [...] Read more.
The area of smart homes is one of the most popular for deploying smart connected devices. One of the most vulnerable aspects of smart homes is access control. Recent advances in IoT have led to several access control models being developed or adapted to IoT from other domains, with few specifically designed to meet the challenges of smart homes. Most of these models use role-based access control (RBAC) or attribute-based access control (ABAC) models. As of now, it is not clear what the advantages and disadvantages of ABAC over RBAC are in general, and in the context of smart-home IoT in particular. In this paper, we introduce HABACα, an attribute-based access control model for smart-home IoT. We formally define HABACα and demonstrate its features through two use-case scenarios and a proof-of-concept implementation. Furthermore, we present an analysis of HABACα as compared to the previously published EGRBAC (extended generalized role-based access control) model for smart-home IoT by first describing approaches for constructing HABACα specification from EGRBAC and vice versa in order to compare the theoretical expressiveness power of these models, and second, analyzing HABACα and EGRBAC models against standard criteria for access control models. Our findings suggest that a hybrid model that combines both HABACα and EGRBAC capabilities may be the most suitable for smart-home IoT, and probably more generally. Full article
(This article belongs to the Special Issue Secure and Trustworthy Cyber–Physical Systems)
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18 pages, 6956 KiB  
Article
Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal
by Ernia Susana, Kalamullah Ramli, Hendri Murfi and Nursama Heru Apriantoro
Information 2022, 13(2), 59; https://doi.org/10.3390/info13020059 - 24 Jan 2022
Cited by 19 | Viewed by 9317
Abstract
Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose [...] Read more.
Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
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18 pages, 2930 KiB  
Article
Apache Spark and MLlib-Based Intrusion Detection System or How the Big Data Technologies Can Secure the Data
by Otmane Azeroual and Anastasija Nikiforova
Information 2022, 13(2), 58; https://doi.org/10.3390/info13020058 - 24 Jan 2022
Cited by 24 | Viewed by 5624
Abstract
Since the turn of the millennium, the volume of data has increased significantly in both industries and scientific institutions. The processing of these volumes and variety of data we are dealing with are unlikely to be accomplished with conventional software solutions. Thus, new [...] Read more.
Since the turn of the millennium, the volume of data has increased significantly in both industries and scientific institutions. The processing of these volumes and variety of data we are dealing with are unlikely to be accomplished with conventional software solutions. Thus, new technologies belonging to the big data processing area, able to distribute and process data in a scalable way, are integrated into classical Business Intelligence (BI) systems or replace them. Furthermore, we can benefit from big data technologies to gain knowledge about security, which can be obtained from massive databases. The paper presents a security-relevant data analysis based on the big data analytics engine Apache Spark. A prototype intrusion detection system is developed aimed at detecting data anomalies through machine learning by using the k-means algorithm for clustering analysis implemented in Sparks MLlib. The extraction of features to detect anomalies is currently challenging because the problem of detecting anomalies is not actively and exhaustively monitored. The detection of abnormal data can be effectuated by using relevant data that are already in companies’ and scientific organizations’ possession. Their interpretation and further processing in a continuous manner can sufficiently contribute to anomaly and intrusion detection. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
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22 pages, 394 KiB  
Article
A Literature Survey of Recent Advances in Chatbots
by Guendalina Caldarini, Sardar Jaf and Kenneth McGarry
Information 2022, 13(1), 41; https://doi.org/10.3390/info13010041 - 15 Jan 2022
Cited by 117 | Viewed by 38471
Abstract
Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and [...] Read more.
Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation. Full article
(This article belongs to the Special Issue Natural Language Interface for Smart Systems)
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23 pages, 1395 KiB  
Article
A Comparative Study of Users versus Non-Users’ Behavioral Intention towards M-Banking Apps’ Adoption
by Vaggelis Saprikis, Giorgos Avlogiaris and Androniki Katarachia
Information 2022, 13(1), 30; https://doi.org/10.3390/info13010030 - 11 Jan 2022
Cited by 26 | Viewed by 5247
Abstract
The banking sector has been considered as one of the primary adopters of Information and Communications Technologies. Especially during the last years, they have invested a lot into the digital transformation of their business process. Concerning their retail customers, banks realized very early [...] Read more.
The banking sector has been considered as one of the primary adopters of Information and Communications Technologies. Especially during the last years, they have invested a lot into the digital transformation of their business process. Concerning their retail customers, banks realized very early the great potential abilities to provide value added self-services functions via mobile devices, mainly smartphones to them; thus, they have invested a lot into m-banking apps’ functionality. Furthermore, the COVID-19 pandemic has brought out different ways for financial transactions and even more mobile users have taken advantage of m-banking app services. Thus, the purpose of this empirical paper is to investigate the determinants that impact individuals on adopting or not m-banking apps. Specifically, it examines two groups of individuals, users (adopters) and non-users (non-adopters) of m-banking apps, and aims to reveal if there are differences and similarities between the factors that impact them on adopting or not this type of m-banking services. To our knowledge, this is the second scientific attempt where these two groups of individuals have been compared on this topic. The paper proposes a comprehensive conceptual model by extending Venkatech’s et al. (2003) Unified Theory of Acceptance and Use of Technology (UTAUT) with ICT facilitators (i.e., reward and security) and ICT inhibitors (i.e., risk and anxiety), as well as the recommendation factor. However, this study intends to fill the research gap by investigating and proving for the first time the impact of social influence, reward and anxiety factors on behavioral intention, the relationship between risk and anxiety and the impact of behavioral intention on recommendation via the application of Confirmatory Factor Analysis and Structural Equation Modeling (SEM) statistical techniques. The results reveal a number of differences regarding the factors that impact or not these two groups towards m-banking app adoption; thus, it provides new insights regarding m-banking app adoption in a slightly examined scientific field. Thus, the study intends to assist the banking sector in better understanding their customers with the aim to formulate and apply customized m-business strategies and increase not only the adoption of m-banking apps but also the level of their further use. Full article
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15 pages, 581 KiB  
Article
Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection
by Changsong Bing, Yirong Wu, Fangmin Dong, Shouzhi Xu, Xiaodi Liu and Shuifa Sun
Information 2022, 13(1), 25; https://doi.org/10.3390/info13010025 - 09 Jan 2022
Cited by 10 | Viewed by 2837
Abstract
Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on [...] Read more.
Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on rumor detection, propose a dual co-attention-based multi-feature fusion method for rumor detection, and explore the detection capability of the proposed method in early rumor detection tasks. The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding. It simultaneously integrates features from three sources: publishing user profiles, source tweets, and comments. In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively. The bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence. A dual collaborative attention mechanism is used to explore the correlation among publishing user profiles, tweet content, and comments. Then the feature vector is fed into classifier to identify the implicit differences between rumor spreaders and non-rumor spreaders. In this study, we conducted several experiments on the Weibo and CED datasets collected from microblog. The results show that the proposed method achieves the state-of-the-art performance compared with baseline methods, which is 5.2% and 5% higher than the dEFEND. The F1 value is increased by 4.4% and 4%, respectively. In addition, this paper conducts research on early rumor detection tasks, which verifies the proposed method detects rumors more quickly and accurately than competitors. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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33 pages, 3265 KiB  
Review
Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends
by Mohamed Amine Ben Farah, Elochukwu Ukwandu, Hanan Hindy, David Brosset, Miroslav Bures, Ivan Andonovic and Xavier Bellekens
Information 2022, 13(1), 22; https://doi.org/10.3390/info13010022 - 06 Jan 2022
Cited by 40 | Viewed by 17244
Abstract
The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of [...] Read more.
The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted. Full article
(This article belongs to the Special Issue Cyber-Security for the Maritime Industry)
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26 pages, 5706 KiB  
Article
Towards a Bibliometric Mapping of Network Public Opinion Studies
by Yujie Qiang, Xuewen Tao, Xiaoqing Gou, Zhihui Lang and Hui Liu
Information 2022, 13(1), 17; https://doi.org/10.3390/info13010017 - 03 Jan 2022
Cited by 11 | Viewed by 3141
Abstract
To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and [...] Read more.
To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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27 pages, 5291 KiB  
Article
Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence
by Antonio Comi, Alexander Rossolov, Antonio Polimeni and Agostino Nuzzolo
Information 2021, 12(12), 493; https://doi.org/10.3390/info12120493 - 26 Nov 2021
Cited by 15 | Viewed by 3502
Abstract
Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were [...] Read more.
Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented. Full article
(This article belongs to the Special Issue Predictive Analytics and Data Science)
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14 pages, 1227 KiB  
Article
Critical Factors for Predicting Users’ Acceptance of Digital Museums for Experience-Influenced Environments
by Yue Wu, Qianling Jiang, Shiyu Ni and Hui’e Liang
Information 2021, 12(10), 426; https://doi.org/10.3390/info12100426 - 17 Oct 2021
Cited by 20 | Viewed by 3810
Abstract
Digital museums that use modern technology are gradually replacing traditional museums to stimulate personal growth and promote cultural exchange and social enrichment. With the development and popularization of the mobile Internet, user experience has become a concern in this field. From the perspective [...] Read more.
Digital museums that use modern technology are gradually replacing traditional museums to stimulate personal growth and promote cultural exchange and social enrichment. With the development and popularization of the mobile Internet, user experience has become a concern in this field. From the perspective of the dynamic stage of user experience, in this study, we expand ECM and TAM by combining the characteristics of users and systems, thereby, constructing the theoretical model and 12 hypotheses about the influencing factors of users’ continuance intentions toward digital museums. A total of 262 valid questionnaires were collected, and the structural equation model tested the model. This study identifies variables that play a role and influence online behavior in a specific experiential environment: (1) Perceived playfulness, perceived usefulness, and satisfaction are the critical variables that affect users’ continuance intentions. (2) Expectation confirmation has a significant influence on perceived playfulness, perceived ease of use, and satisfaction. (3) Media richness is an essential driver of confirmation, perceived ease of use, and perceived usefulness. The conclusions can be used as a reference for managers to promote the construction and innovation of digital museums and provide a better experience to meet users’ needs. Full article
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13 pages, 505 KiB  
Article
Integrating Comprehensive Human Oversight in Drone Deployment: A Conceptual Framework Applied to the Case of Military Surveillance Drones
by Ilse Verdiesen, Andrea Aler Tubella and Virginia Dignum
Information 2021, 12(9), 385; https://doi.org/10.3390/info12090385 - 21 Sep 2021
Cited by 9 | Viewed by 3483
Abstract
Accountability is a value often mentioned in the debate on intelligent systems and their increased pervasiveness in our society. When focusing specifically on autonomous systems, a critical gap emerges: although there is much work on governance and attribution of accountability, there is a [...] Read more.
Accountability is a value often mentioned in the debate on intelligent systems and their increased pervasiveness in our society. When focusing specifically on autonomous systems, a critical gap emerges: although there is much work on governance and attribution of accountability, there is a significant lack of methods for the operationalisation of accountability within the socio-technical layer of autonomous systems. In the case of autonomous unmanned aerial vehicles or drones—the critical question of how to maintain accountability as they undertake fully autonomous flights becomes increasingly important as their uses multiply in both the commercial and military fields. In this paper, we aim to fill the operationalisation gap by proposing a socio-technical framework to guarantee human oversight and accountability in drone deployments, showing its enforceability in the real case of military surveillance drones. By keeping a focus on accountability and human oversight as values, we align with the emphasis placed on human responsibility, while requiring a concretisation of what these principles mean for each specific application, connecting them with concrete socio-technical requirements. In addition, by constraining the framework to observable elements of pre- and post-deployment, we do not rely on assumptions made on the internal workings of the drone nor the technical fluency of the operator. Full article
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13 pages, 1934 KiB  
Article
Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems
by Lilian Asimwe Leonidas and Yang Jie
Information 2021, 12(8), 302; https://doi.org/10.3390/info12080302 - 28 Jul 2021
Cited by 13 | Viewed by 4765
Abstract
In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of [...] Read more.
In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of other target objects. Hence, there is still a need to do more research on solving the above problems to prevent collisions in inland waterways. In this paper, we introduce a new convolutional neural network classification algorithm capable of classifying five classes of ships, including cargo, military, carrier, cruise and tanker ships, in inland waterways. The game of deep learning ship dataset, which is a public dataset originating from Kaggle, has been used for all experiments. Initially, the five pretrained models (which are AlexNet, VGG, Inception V3 ResNet and GoogleNet) were used on the dataset in order to select the best model based on its performance. Resnet-152 achieved the best model with an accuracy of 90.56%, and AlexNet achieved a lower accuracy of 63.42%. Furthermore, Resnet-152 was improved by adding a classification block which contained two fully connected layers, followed by ReLu for learning new characteristics of our training dataset and a dropout layer to resolve the problem of a diminishing gradient. For generalization, our proposed method was also tested on the MARVEL dataset, which consists of more than 10,000 images and 26 categories of ships. Furthermore, the proposed algorithm was compared with existing algorithms and obtained high performance compared with the others, with an accuracy of 95.8%, precision of 95.83%, recall of 95.80%, specificity of 95.07% and F1 score of 95.81%. Full article
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20 pages, 5669 KiB  
Article
BCoT Sentry: A Blockchain-Based Identity Authentication Framework for IoT Devices
by Liangqin Gong, Daniyal M. Alghazzawi and Li Cheng
Information 2021, 12(5), 203; https://doi.org/10.3390/info12050203 - 10 May 2021
Cited by 27 | Viewed by 4678
Abstract
In Internet of Things (IoT) environments, privacy and security are among some of the significant challenges. Recently, several studies have attempted to apply blockchain technology to increase IoT network security. However, the lightweight feature of IoT devices commonly fails to meet computational intensive [...] Read more.
In Internet of Things (IoT) environments, privacy and security are among some of the significant challenges. Recently, several studies have attempted to apply blockchain technology to increase IoT network security. However, the lightweight feature of IoT devices commonly fails to meet computational intensive requirements for blockchain-based security models. In this work, we propose a mechanism to address this issue. We design an IoT blockchain architecture to store device identity information in a distributed ledger. We propose a Blockchain of Things (BCoT) Gateway to facilitate the recording of authentication transactions in a blockchain network without modifying existing device hardware or applications. Furthermore, we introduce a new device recognition model that is suitable for blockchain-based identity authentication, where we employ a novel feature selection method for device traffic flow. Finally, we develop the BCoT Sentry framework as a reference implementation of our proposed method. Experiment results verify the feasibility of our proposed framework. Full article
(This article belongs to the Section Information and Communications Technology)
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29 pages, 77707 KiB  
Article
Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information
by Hussein Ali Ameen, Abd Kadir Mahamad, Sharifah Saon, Rami Qays Malik, Zahraa Hashim Kareem, Mohd Anuaruddin Bin Ahmadon and Shingo Yamaguchi
Information 2021, 12(5), 194; https://doi.org/10.3390/info12050194 - 29 Apr 2021
Cited by 4 | Viewed by 4052
Abstract
Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant [...] Read more.
Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios. Full article
(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)
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12 pages, 452 KiB  
Article
A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection
by Vasileios Kouliaridis and Georgios Kambourakis
Information 2021, 12(5), 185; https://doi.org/10.3390/info12050185 - 25 Apr 2021
Cited by 56 | Viewed by 7341
Abstract
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on [...] Read more.
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field. Full article
(This article belongs to the Special Issue Detecting Attack and Incident Zone System)
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30 pages, 10142 KiB  
Article
Virtual Restoration and Virtual Reconstruction in Cultural Heritage: Terminology, Methodologies, Visual Representation Techniques and Cognitive Models
by Eva Pietroni and Daniele Ferdani
Information 2021, 12(4), 167; https://doi.org/10.3390/info12040167 - 13 Apr 2021
Cited by 48 | Viewed by 9659
Abstract
Today, the practice of making digital replicas of artworks and restoring and recontextualizing them within artificial simulations is widespread in the virtual heritage domain. Virtual reconstructions have achieved results of great realistic and aesthetic impact. Alongside the practice, a growing methodological awareness has [...] Read more.
Today, the practice of making digital replicas of artworks and restoring and recontextualizing them within artificial simulations is widespread in the virtual heritage domain. Virtual reconstructions have achieved results of great realistic and aesthetic impact. Alongside the practice, a growing methodological awareness has developed of the extent to which, and how, it is permissible to virtually operate in the field of restoration, avoid a false sense of reality, and preserve the reliability of the original content. However, there is not yet a full sharing of meanings in virtual restoration and reconstruction domains. Therefore, this article aims to clarify and define concepts, functions, fields of application, and methodologies. The goal of virtual heritage is not only producing digital replicas. In the absence of materiality, what emerges as a fundamental value are the interaction processes, the semantic values that can be attributed to the model itself. The cognitive process originates from this interaction. The theoretical discussion is supported by exemplar case studies carried out by the authors over almost twenty years. Finally, the concepts of uniqueness and authenticity need to be again pondered in light of the digital era. Indeed, real and virtual should be considered as a continuum, as they exchange information favoring new processes of interaction and critical thinking. Full article
(This article belongs to the Special Issue Virtual Reality Technologies and Applications for Cultural Heritage)
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