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Computers, Volume 10, Issue 6 (June 2021) – 14 articles

Cover Story (view full-size image): Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. Here, three approaches for using quantum machine learning for a specific task, indoor-outdoor detection, in mobile networks are proposed and evaluated. Where current quantum computers are still limited in scale, we show the potential that the approaches have when larger systems are available. View this paper
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Article
Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals
Computers 2021, 10(6), 82; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060082 - 17 Jun 2021
Viewed by 400
Abstract
Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the [...] Read more.
Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks. Full article
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Article
Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems
Computers 2021, 10(6), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060081 - 16 Jun 2021
Viewed by 486
Abstract
With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces [...] Read more.
With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi-Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model implemented included an Adaptive Neuro Fuzzy Inference System (ANFIS), which was used in different configurations to fuse data from an Electroencephalogram (EEG) model’s output, four eye activity features and a control input feature. The online validation of the ANFIS models produced good results, while the best performing model (containing all four eye activity features and the control input feature) showed an average Mean Absolute Error (MAE) = 0.67 ± 0.18 and Correlation Coefficient (CC) = 0.71 ± 0.15. The remaining six ANFIS models included data from the EEG model’s output, which had an offset discrepancy. This resulted in an equivalent offset for the online multimodal fusion. Nonetheless, the efficacy of these ANFIS models could be confirmed by the pairwise correlation with the task level, where one model demonstrated a CC = 0.77 ± 0.06, which was the highest among all of the ANFIS models tested. Hence, this study demonstrates the suitability for online multimodal fusion of features extracted from EEG signals, eye activity and control inputs to produce an accurate and repeatable inference of MWL. Full article
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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Article
On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment: A Pilot Study
Computers 2021, 10(6), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060080 - 12 Jun 2021
Viewed by 581
Abstract
Due to the advances in head-mounted displays (HMDs), hardware and software technologies, and mobile connectivity, virtual reality (VR) applications such as viewing 360° videos on HMDs have seen an increased interest in a wide range of consumer and vertical markets. Quality assessment of [...] Read more.
Due to the advances in head-mounted displays (HMDs), hardware and software technologies, and mobile connectivity, virtual reality (VR) applications such as viewing 360° videos on HMDs have seen an increased interest in a wide range of consumer and vertical markets. Quality assessment of digital media systems and services related to immersive visual stimuli has been one of the challenging problems of multimedia signal processing. Specifically, subjective quality assessment of 360° videos presented on HMDs is needed to obtain a ground truth on the visual quality as perceived by humans. Standardized test methodologies to assess the subjective quality of 360° videos on HMDs are currently not as developed as for conventional videos and are subject to further study. In addition, subjective tests related to quality assessment of 360° videos are commonly conducted with participants seated on a chair but neglect other options of consumption such as standing viewing. In this paper, we compare the effect that standing and seated viewing of 360° videos on an HMD has on subjective quality assessment. A pilot study was conducted to obtain psychophysical and psychophysiological data that covers explicit and implicit responses of the participants to the shown 360° video stimuli with different quality levels. The statistical analysis of the data gathered in the pilot study is reported in terms of average rating times, mean opinion scores, standard deviation of opinion scores, head movements, pupil diameter, galvanic skin response (GSR), and simulator sickness scores. The results indicate that the average rating times consumed for 360° video quality assessment are similar for standing and seated viewing. Further, the participants showed higher resolving power among different 360° video quality levels and were more confident about the given opinion scores for seated viewing. On the other hand, a larger scene exploration of 360° videos was observed for standing viewing which appears to distract from the quality assessment task. A slightly higher pupil dilation was recorded for standing viewing which suggests a slightly more immersed experience compared to seated viewing. GSR data indicate a lower degree of emotional arousal in seated viewing which seems to allow the participants to better conduct the quality assessment task. Similarly, simulator sickness symptoms are kept significantly lower when seated. The pilot study also contributes to a holistic view of subjective quality assessment and provides indicative ground truth that can guide the design of large-scale subjective tests. Full article
(This article belongs to the Special Issue Advances in Seated Virtual Reality)
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Article
CBAM: A Contextual Model for Network Anomaly Detection
Computers 2021, 10(6), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060079 - 11 Jun 2021
Viewed by 632
Abstract
Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably [...] Read more.
Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift. Full article
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Article
Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images
Computers 2021, 10(6), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060078 - 11 Jun 2021
Viewed by 479
Abstract
This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the dataset, tensor factorization preserves the spectral-spatial relationship which we intend to exploit. We used a [...] Read more.
This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the dataset, tensor factorization preserves the spectral-spatial relationship which we intend to exploit. We used a rank-(L, L, 1) decomposition, which approximates the original tensor as a sum of R components. Each component is a tensor resulting from the multiplication of a low-rank spatial representation and a spectral vector. Our approach uses spatial factors to identify high abundance areas where pure pixels (endmembers) may lie. Unmixing is done by applying Fully Constrained Least Squares such that abundance maps are produced for each inferred endmember. The results of this method are compared against other approaches based on non-negative matrix and tensor factorization. We observed a significant reduction of spectral angle distance for extracted endmembers and equal or better RMSE for abundance maps as compared with existing benchmarks. Full article
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Article
Functional Design Employing Miniaturized Electronics with Wireless Signal Provision to a Smartphone for a Strain-Based Measuring System for Ski Poles
Computers 2021, 10(6), 77; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060077 - 11 Jun 2021
Viewed by 478
Abstract
The individual monitoring of cross-country skiers’ technique-related parameters is crucial to identifying possible athlete-individual deficits that need to be corrected in order to optimize the athlete’s performance in competition. To be able to record relevant biomechanical parameters during training in the field, the [...] Read more.
The individual monitoring of cross-country skiers’ technique-related parameters is crucial to identifying possible athlete-individual deficits that need to be corrected in order to optimize the athlete’s performance in competition. To be able to record relevant biomechanical parameters during training in the field, the development of measuring systems exploiting the athlete’s full potential is the key. Known mobile monitoring systems for measuring forces on ski poles use comparably heavy uniaxial load cells mounted on the pole with a data logger also attached to the pole or carried by the athlete. Measurements that are more accurate can be acquired using wire-based systems. However, wire-based systems are highly immobile and only usable when the athletes undergo a stationary test, e.g., on a treadmill. This paper focuses on the functional design of a measuring system using specialized, miniaturized electronics for acquiring data from strain sensors. These data are then used to determine the technique-related parameters pole force and angle of bend. The functional design is also capable of transmitting the acquired data wirelessly via Bluetooth to a smartphone that runs a proprietary app. This approach is advantageous regarding mass, dynamic behavior, analyzing functionality, and signal processing compared to the state of the art. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2020)
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Article
Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection
Computers 2021, 10(6), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060076 - 11 Jun 2021
Viewed by 497
Abstract
We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in [...] Read more.
We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four times higher throughput in some key cases. However, OPS for the foreground traffic (e.g., TCP CUBIC, TCP BBR, UDT) depends on knowledge about the network protocols used by the background traffic (i.e., other users). Therefore, we build and empirically evaluate several machine-learned (ML) classifiers, trained on local round-trip time (RTT) time-series data gathered using active probing, to recognize the mix of network protocols in the background with an accuracy of up to 0.96. Full article
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Article
Motivation, Stress and Impact of Online Teaching on Italian Teachers during COVID-19
Computers 2021, 10(6), 75; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060075 - 11 Jun 2021
Cited by 1 | Viewed by 693
Abstract
The use of digital technology as the only communication and relationship channel in work, school and social contexts is bringing out dynamics that are sometimes in contrast with each other. The purpose of this article is to investigate the impact of digital technology [...] Read more.
The use of digital technology as the only communication and relationship channel in work, school and social contexts is bringing out dynamics that are sometimes in contrast with each other. The purpose of this article is to investigate the impact of digital technology on teachers’ school practices in the context of COVID-19. This impact was studied in relation to the constructs of motivation, perceived stress, sense of self-efficacy and resistance to/acceptance of technologies. This study examined the role played by the massive and coercive use of digital technologies (and the relationship with innovation and change) in predicting motivation and perceived stress among teachers. To this end, the impact of digital technologies on motivation and perceived stress were explored in the sample. A questionnaire consisting of three scales was administered to 688 Italian school teachers of all educational levels (from childhood to upper-secondary school), who completed a socio-demographic section, a section on the scale of the impact of technology and distance learning, a perceived stress scale and items on motivation and professional development. Descriptive and inferential analyses were applied to the data. Key findings indicated that the impact of digital technologies during the pandemic negatively correlates with both perceived stress and motivation. Practical implications were suggested to help teachers develop functional coping styles to cope with technological changes in work and life contexts. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies)
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Article
Neural Network Methodology for the Identification and Classification of Lipopeptides Based on SMILES Annotation
Computers 2021, 10(6), 74; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060074 - 10 Jun 2021
Viewed by 448
Abstract
Artificial Neural Networks can be applied for the identification and classification of prospective drug candidates such as complex compounds, including lipopeptide, based on their SMILES string representation. The training of neural networks is done with SMILES strings, which are predictive of structural identification; [...] Read more.
Artificial Neural Networks can be applied for the identification and classification of prospective drug candidates such as complex compounds, including lipopeptide, based on their SMILES string representation. The training of neural networks is done with SMILES strings, which are predictive of structural identification; the ANNs are efficient of correctly classifying all compounds, substructures and their analogues distinguishing the drugs based upon atomic organization to obtain lead optimization in drug discovery. The proficiency of the trained ANN models in recognizing and classifying the analogous compounds was tested for analysis of similar compounds, which were not taken previously for training and achieved results with correct classification in the validation set. The best result was achieved with 10 numbers of hidden layers. The R2 value for training is 0.90586; the R2 value for testing is 0.99508; the R2 value after validation is 0.94151; the final value of R2 for total sets is 0.89456. The graphs are plotted between 21 epochs and mean square error (MSE) to report the performance of the model. The value of 798.1735 for the gradient of the curve after 21 iterations and 6 validation checks was obtained. A successful model was developed for the identification and classification of lipopeptides from their SMILES annotation that efficiently classifies similar compounds and supports in decision making for analogue-based drug discovery. This will help in appropriate lead optimization studies for the prediction of potential anticancer and antimicrobial lipopeptide-based therapeutics. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
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Article
To Sit or Not to Sit in VR: Analyzing Influences and (Dis)Advantages of Posture and Embodied Interaction
Computers 2021, 10(6), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060073 - 03 Jun 2021
Viewed by 695
Abstract
Virtual Reality (VR) users typically either sit or stand/walk when using VR; however, the impact of this is little researched, and there is a lack of any broad or systematic analysis of how this difference in physical posture might affect user experience and [...] Read more.
Virtual Reality (VR) users typically either sit or stand/walk when using VR; however, the impact of this is little researched, and there is a lack of any broad or systematic analysis of how this difference in physical posture might affect user experience and behavior. To address this gap, we propose such a systematic analysis that was refined through discussions and iterations during a dedicated workshop with VR experts. This analysis was complemented by an online survey to integrate the perspectives of a larger and more diverse group of VR experts, including developers and power users. The result is a validated expert assessment of the impact of posture and degree of embodiment on the most relevant aspects of VR experience and behavior. In particular, we posit potential strong effects of posture on user comfort, safety, self-motion perception, engagement, and accessibility. We further argue that the degree of embodiment can strongly impact cybersickness, locomotion precision, safety, self-motion perception, engagement, technical complexity, and accessibility. We provide a compact visualization of key findings and discuss areas where posture and embodiment do or do not have a known influence, as well as highlight open questions that could guide future research and VR design efforts. Full article
(This article belongs to the Special Issue Advances in Seated Virtual Reality)
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Article
EIPPM—The Executable Integrative Product-Production Model
Computers 2021, 10(6), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060072 - 27 May 2021
Viewed by 578
Abstract
In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see [...] Read more.
In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of SBE for the development of production systems (PS). Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain “PSs” together with the knowledge of the corresponding “product” using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle of product and PS, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g., through feedback), a design of experiment is made possible, whereby unconventional solutions are also considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic disciplines through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-PS combinations, automatic generation of simulation models starting with the planning phase and feedback from simulation-based optimization back into the data model. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2020)
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Article
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
Computers 2021, 10(6), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060071 - 26 May 2021
Viewed by 775
Abstract
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The [...] Read more.
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available. Full article
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Article
A Proposal to Improve Interoperability in the Industry 4.0 Based on the Open Platform Communications Unified Architecture Standard
Computers 2021, 10(6), 70; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060070 - 21 May 2021
Viewed by 722
Abstract
The introduction of the Industrial Internet of Things in the factory environment is one of the most important features of the fourth industrial revolution. The main aim is the integration of sensor and actuator devices, based on the Internet of Things, with the [...] Read more.
The introduction of the Industrial Internet of Things in the factory environment is one of the most important features of the fourth industrial revolution. The main aim is the integration of sensor and actuator devices, based on the Internet of Things, with the industrial applications used for factory processes. This goal may be reached only if interoperability between the communication protocols existing in the domains of industrial applications and the Internet of Things is achieved. Open Platform Communications Unified Architecture (OPC UA) is considered one of the main reference communication standards in Industry 4.0 among industrial applications. Within the Internet of Things domain, the oneM2M communication protocol has been defined for solving the current fragmentation of this domain in the information exchange between sensor and actuator devices. Interoperability between these two communication protocols may allow integration of the industrial applications with Internet of Things-based devices. The current state of the art does not present any interoperability solution to allow the information produced by oneM2M-based devices to be consumed by OPC UA industrial applications. In order to reach this aim, the paper proposes a novel solution based on the use of a standard interworking proxy. The paper will describe this solution and the relevant software implementation. Full article
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Article
Educational Challenges for Computational Thinking in K–12 Education: A Systematic Literature Review of “Scratch” as an Innovative Programming Tool
Computers 2021, 10(6), 69; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060069 - 21 May 2021
Viewed by 628
Abstract
The use of information and communications technologies (ICTs) has emerged as an educational response amidst the COVID-19 pandemic, providing students the technological tools that enable them to acquire or strengthen the necessary digital skills to develop computational knowledge. The purpose of this study [...] Read more.
The use of information and communications technologies (ICTs) has emerged as an educational response amidst the COVID-19 pandemic, providing students the technological tools that enable them to acquire or strengthen the necessary digital skills to develop computational knowledge. The purpose of this study was to analyze Scratch, a programming language used to foster the teaching of computational thinking, particularly in K–12 education. A systematic literature review (SLR) was conducted, identifying 30 articles on the topic of Scratch and computational thinking in the database ProQuest Central from January 2010 to May 2020. These articles were analyzed to identify the use of Scratch worldwide and the educational impact it has on computational thinking, specifically in K–12 education. The results highlight the following: (1) countries which incorporated Scratch into their teachers’ study plans (curricula); (2) the transformation of learning environments that Scratch promotes; and (3) the importance of incorporating tools like Scratch in the current curricula and, more importantly, developing the framework for innovative ICTs capable of transforming education. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies)
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