Computers

Open AccessArticle

The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain

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Vladislav N. Gezha

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Ivan V. Kozitsin

Computers 2023, 12(6), 116; https://doi.org/10.3390/computers12060116 - 02 Jun 2023

Abstract

The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line [...] Read more.

The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention. Full article

(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)

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Open AccessArticle

Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks

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Computers 2023, 12(6), 115; https://doi.org/10.3390/computers12060115 - 29 May 2023

Abstract

The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine [...] Read more.

The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks. Full article

(This article belongs to the Special Issue Software-Defined Internet of Everything)

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Open AccessReview

To Wallet or Not to Wallet: The Debate over Digital Health Information Storage

by

Jasna Karacic Zanetti

and

Rui Nunes

Computers 2023, 12(6), 114; https://doi.org/10.3390/computers12060114 - 28 May 2023

Abstract

The concept of the health wallet, a digital platform that consolidates health-related information, has garnered significant attention in the past year. Electronic health data storage and transmission have become increasingly prevalent in the healthcare industry, with the potential to revolutionize healthcare delivery. This [...] Read more.

The concept of the health wallet, a digital platform that consolidates health-related information, has garnered significant attention in the past year. Electronic health data storage and transmission have become increasingly prevalent in the healthcare industry, with the potential to revolutionize healthcare delivery. This paper emphasizes the significance of recognizing and addressing the ethical implications of digital health technologies and prioritizes ethical considerations in their development. The adoption of health wallets has theoretical contributions, including the development of personalized medicine through comprehensive data collection, reducing medical errors through consolidated information, and enabling research for the improvement of existing treatments and interventions. Health wallets also empower individuals to manage their own health by providing access to their health data, allowing them to make informed decisions. The findings herein emphasize the importance of informing patients about their rights to control their health data and have access to it while protecting their privacy and confidentiality. This paper stands out by presenting practical recommendations for healthcare organizations and policymakers to ensure the safe and effective implementation of health wallets. Full article

(This article belongs to the Special Issue e-health Pervasive Wireless Applications and Services (e-HPWAS'22))

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Open AccessArticle

Prototype of a Recommendation Model with Artificial Intelligence for Computational Thinking Improvement of Secondary Education Students

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Computers 2023, 12(6), 113; https://doi.org/10.3390/computers12060113 - 26 May 2023

Abstract

There is a growing interest in finding new ways to address the difficult task of introducing programming to secondary students for the first time to improve students’ computational thinking (CT) skills. Therefore, extensive research is required in this field. Worldwide, new ways to [...] Read more.

There is a growing interest in finding new ways to address the difficult task of introducing programming to secondary students for the first time to improve students’ computational thinking (CT) skills. Therefore, extensive research is required in this field. Worldwide, new ways to address this difficult task have been developed: visual execution environments and approaches by text programming or visual programming are among the most popular. This paper addresses the complex task by using a visual execution environment (VEE) to introduce the first programming concepts that should be covered in any introductory programming course. These concepts include variables, input and output, conditionals, loops, arrays, functions, and files. This study explores two approaches to achieve this goal: visual programming (using Scratch) and text programming (using Java) to improve CT. Additionally, it proposes an AI recommendation model into the VEE to further improve the effectiveness of developing CT among secondary education students. This integrated model combines the capabilities of an AI learning system module and a personalized learning module to better address the task at hand. To pursue this task, an experiment has been carried out among 23 preservice secondary teachers’ students in two universities, one in Madrid, Spain, and the other in Galway, Ireland. The overall results showed a significant improvement in the Scratch group. However, when analyzing the results based on specific programming concepts, significance was observed only in the Scratch group, specifically for the Loop concept. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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Open AccessArticle

Image Denoising by Deep Convolution Based on Sparse Representation

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Computers 2023, 12(6), 112; https://doi.org/10.3390/computers12060112 - 24 May 2023

Abstract

Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of noisy images. The extracted features [...] Read more.

Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of noisy images. The extracted features are acquired through prior and sparse representation theory for image reconstruction. Effective separation of the image and noise is achieved using an end-to-end network of dilated convolution and fully connected layers. Several experiments were performed on public images subject to various levels of Gaussian noise, in order to evaluate the effectiveness of the proposed approach. The results indicated that our algorithm achieved a high peak signal-to-noise ratio (PSNR) and significantly improved the visual effects of the images. Our study supports the effectiveness of our approach and substantiates its potential to be applied to a broad spectrum of image processing tasks. Full article

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Open AccessArticle

Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing

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Maria Nefeli Nikiforos

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Konstantina Deliveri

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Katia Lida Kermanidis

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Adamantia Pateli

Computers 2023, 12(6), 111; https://doi.org/10.3390/computers12060111 - 24 May 2023

Abstract

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational [...] Read more.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, natural language processing, and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of machine learning experiments with a domain-specific textual data set while considering two research directions: a. prediction analysis and b. data balancing. Wrong prediction analysis and the features that contributed to misclassification, along with correct prediction analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel four-step methodology was proposed in this paper for the first time, which consists of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtained better results than data undersampling in imbalanced data sets, while hybrid approaches performed reasonably well. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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Open AccessArticle

Persistence Landscapes—Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems

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Computers 2023, 12(6), 110; https://doi.org/10.3390/computers12060110 - 23 May 2023

Abstract

As more and more devices are being deployed across networks to gather data and use them to perform intelligent tasks, it is vital to have a tool to perform real-time data analysis. Data are the backbone of Machine Learning models, the core of [...] Read more.

As more and more devices are being deployed across networks to gather data and use them to perform intelligent tasks, it is vital to have a tool to perform real-time data analysis. Data are the backbone of Machine Learning models, the core of intelligent systems. Therefore, verifying whether the data being gathered are similar to those used for model building is essential. One fantastic tool for the performance of data analysis is the 0-Dimensional Persistent Diagrams, which can be computed in a Resource-Scarce Embedded System (RSES), a set of memory and processing-constrained devices that are used in many IoT applications because they are cost-effective and reliable. However, it is challenging to compare Persistent Diagrams, and Persistent Landscapes are used because they allow Persistent Diagrams to be passed to a space where the mean concept is well-defined. The following work shows how one can perform a Persistent Landscape analysis in an RSES. It also shows that the distance between two Persistent Landscapes makes it possible to verify whether two devices collect the same data. The main contribution of this work is the implementation of Persistent Landscape analysis in an RSES, which is not provided in the literature. Moreover, it shows that devices can now verify, in real-time, whether they can trust the data being collected to perform the intelligent task they were designed to, which is essential in any system to avoid bugs or errors. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems 2023)

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Open AccessArticle

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

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Computers 2023, 12(5), 109; https://doi.org/10.3390/computers12050109 - 22 May 2023

Abstract

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. [...] Read more.

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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Open AccessArticle

Investigating the Cultural Impact on Predicting Crowd Behavior

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Fatima Jafar Muhdher

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Osama Ahmed Abulnaja

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Fatmah Abdulrahman Baothman

Computers 2023, 12(5), 108; https://doi.org/10.3390/computers12050108 - 21 May 2023

Abstract

The Cultural Crowd–Artificial Neural Network (CC-ANN) takes the cultural dimensions of a crowd into account, based on Hofstede Cultural Dimensions (HCDs), to predict social and physical behavior concerning cohesion, collectivity, speed, and distance. This study examines the impact of applying the CC-ANN learning [...] Read more.

The Cultural Crowd–Artificial Neural Network (CC-ANN) takes the cultural dimensions of a crowd into account, based on Hofstede Cultural Dimensions (HCDs), to predict social and physical behavior concerning cohesion, collectivity, speed, and distance. This study examines the impact of applying the CC-ANN learning model on more cultures to test the effect of predicting crowd behavior and the relationships among their characteristics. Our previous work which applied the CC-ANN only included eight nations using the six HCDs. In this paper, we including the United Arab Emirates (UAE) in the CC-ANN as a new culture which aided a comparative study with four HCDs, with and without the UAE, using Mean Squared Error (MSE) for evaluation. The results indicated that most of the best-case experiments involved the UAE having the lowest MSE: 0.127, 0.014, and 0.010, which enhanced the CC-ANN model’s ability to predict crowd behavior. Moreover, the links between the cultural, sociological, and physical properties of crowds can be seen from a broader perspective with stronger correlations using the CC-ANN in more countries with diverse cultures. Full article

(This article belongs to the Special Issue Human Understandable Artificial Intelligence)

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Open AccessArticle

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

by

Moez Krichen

Computers 2023, 12(5), 107; https://doi.org/10.3390/computers12050107 - 18 May 2023

Abstract

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of [...] Read more.

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security. Full article

(This article belongs to the Special Issue Using New Technologies on Cyber Security Solutions)

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Open AccessArticle

Peer-to-Peer Federated Learning for COVID-19 Detection Using Transformers

by

Mohamed Chetoui

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Moulay A. Akhloufi

Computers 2023, 12(5), 106; https://doi.org/10.3390/computers12050106 - 17 May 2023

Abstract

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of [...] Read more.

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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Open AccessArticle

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

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Tarik El Lel

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Mominul Ahsan

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Julfikar Haider

Computers 2023, 12(5), 105; https://doi.org/10.3390/computers12050105 - 16 May 2023

Abstract

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to [...] Read more.

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19. Full article

(This article belongs to the Special Issue Uncertainty Aware Artificial Intelligence)

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Open AccessReview

Pressure-Based Posture Classification Methods and Algorithms: A Systematic Review

by

Luís Fonseca

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Fernando Ribeiro

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José Metrôlho

Computers 2023, 12(5), 104; https://doi.org/10.3390/computers12050104 - 15 May 2023

Abstract

There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related [...] Read more.

There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related disorders are a great example where tedious tasks could be facilitated by machine learning algorithms, as suggested by many authors, by providing information on the posture of a particular bedded patient to health professionals. To assess the already existing studies in this field, this study provides a systematic review where the literature is analyzed to find correlations between the various factors involved in the making of such a system and how they perform. The overall findings suggest that there is only a significant relationship between the postures considered for classification and the resulting accuracy, despite some other factors such as the amount of data available providing some differences according to the type of algorithm used, with neural networks needing larger datasets. This study aims to increase awareness in this field and give future researchers information based on previous works’ strengths and limitations while giving some suggestions based on the literature review. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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Open AccessArticle

EEGT: Energy Efficient Grid-Based Routing Protocol in Wireless Sensor Networks for IoT Applications

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Computers 2023, 12(5), 103; https://doi.org/10.3390/computers12050103 - 12 May 2023

Abstract

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, [...] Read more.

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems 2023)

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Open AccessArticle

Info-Autopoiesis and the Limits of Artificial General Intelligence

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Jaime F. Cárdenas-García

Computers 2023, 12(5), 102; https://doi.org/10.3390/computers12050102 - 07 May 2023

Abstract

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might [...] Read more.

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background, as well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently, the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus, there is a need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question needs to be resolved: what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that, without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes info-autopoiesis, the self-referential, recursive, and interactive process of self-production of information, as the needed general framework. Info-autopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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Open AccessArticle

Application of GNS3 to Study the Security of Data Exchange between Power Electronic Devices and Control Center

by

Ivan Nedyalkov

Computers 2023, 12(5), 101; https://doi.org/10.3390/computers12050101 - 05 May 2023

Abstract

This paper proposes the use of the GNS3 IP network modeling platform to study/verify whether the exchanged information between power electronic devices and a control center (Monitoring and Control Centre) is secure. For the purpose of this work, a power distribution unit (PDU) [...] Read more.

This paper proposes the use of the GNS3 IP network modeling platform to study/verify whether the exchanged information between power electronic devices and a control center (Monitoring and Control Centre) is secure. For the purpose of this work, a power distribution unit (PDU) and a UPS (Uninterruptable Power Supply) that are used by internet service providers are studied. Capsa Free network analyzer and Wireshark network protocol analyzer were used as supporting tools. A working model of an IP network in GNS3 has been created through which this research has been carried out. In addition to checking whether the exchanged information is secure, a characterization of the generated traffic has been made, showing results for the generated traffic and which ports generate the most traffic. These carried-outstudies show that the exchanged information is not secure. As a way to secure the exchanged information, the use of VPN (Virtual Private Network) technology is proposed; thanks to a VPN, the exchange of information is secure. The obtained results confirm this and validate the applicability of GNS3 to test/study whether data exchange between power electronic devices and a control center is secure. Full article

(This article belongs to the Special Issue Advances in Energy-Efficient Computer and Network Systems)

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Open AccessArticle

Theoretical Models Explaining the Level of Digital Competence in Students

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Marcos Cabezas-González

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Sonia Casillas-Martín

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Ana García-Valcárcel Muñoz-Repiso

Computers 2023, 12(5), 100; https://doi.org/10.3390/computers12050100 - 04 May 2023

Abstract

In the new global scene, digital skills are a key skill for students to seize new learning opportunities, train to meet the demands of the labor market, and compete in the global market, while also communicating effectively in their everyday and academic lives. [...] Read more.

In the new global scene, digital skills are a key skill for students to seize new learning opportunities, train to meet the demands of the labor market, and compete in the global market, while also communicating effectively in their everyday and academic lives. This article presents research aimed at relating the impact of personal variables on the digital competence of technical problem solving in Spanish students from 12 to 14 years old. A quantitative methodology with a cross-sectional design was employed. A sample of 772 students from 18 Spanish educational institutions was used. For data collection, an assessment test was designed (ECODIES^®) based on a validated indicator model to evaluate learners’ digital competence (INCODIES^®), taking as a model the European framework for the development of digital competence. Mediation models were used and theoretical reference models were created. The results allowed us to verify the influence of personal, technology use, and attitudinal variables in the improvement of digital skill in technical problem solving. The findings lead to the conclusion that gender, acquisition of digital devices, and regular use do not determine a better level of competence. Full article

(This article belongs to the Special Issue Immersive Virtual Learning Environments: Connecting Spaces, Time, Technologies, Curriculum, Assessment, and Innovation)

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Open AccessFeature PaperArticle

A Study on Energy Efficiency of a Distributed Processing Scheme for Image-Based Target Recognition for Internet of Multimedia Things

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Adel Soudani

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Manal Alsabhan

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Manan Almusallam

Computers 2023, 12(5), 99; https://doi.org/10.3390/computers12050099 - 04 May 2023

Abstract

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. [...] Read more.

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices. Full article

(This article belongs to the Section Internet of Things (IoT) and Industrial IoT)

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Open AccessArticle

Impact of Image Compression on In Vitro Cell Migration Analysis

by

Ehsaneddin Jalilian

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Michael Linortner

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Andreas Uhl

Computers 2023, 12(5), 98; https://doi.org/10.3390/computers12050098 - 04 May 2023

Abstract

Collective cell movement is an indication of phenomena such as wound healing, embryonic morphogenesis, cancer invasion, and metastasis. Wound healing is a complicated cellular and biochemical procedure in which skin cells migrate from the wound boundaries into the wound area to reconstruct the [...] Read more.

Collective cell movement is an indication of phenomena such as wound healing, embryonic morphogenesis, cancer invasion, and metastasis. Wound healing is a complicated cellular and biochemical procedure in which skin cells migrate from the wound boundaries into the wound area to reconstruct the injured skin layer(s). In vitro analysis of cell migration is an effective assay for measuring changes in cell migratory complement in response to experimental inspections. Open-source segmentation software (e.g., an ImageJ plug-in) is available to analyze images of in vitro scratch wound healing assays; however, often, these tools are error-prone when applied to, e.g., low-contrast, out-of-focus, and noisy images, and require manual tuning of various parameters, which is imprecise, tedious, and time-consuming. We propose two algorithmic methods (namely log gradient segmentation and entropy filter segmentation) for cell segmentation and the subsequent measurement of the collective cell migration in the corresponding microscopic imagery. We further investigate the effects of image compression on the algorithms’ measurement accuracy, applying lossy compression algorithms (the current ISO standards JPEG2000, JPEG, JPEG-XL and AV1, BPG, and WEBP). We aim to identify the most suitable compression algorithm that can be used for this purpose, relating rate–distortion performance as measured in terms of peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index (MS-SSIM) to the segmentation accuracy obtained by the segmentation algorithms. The experimental results show that the log gradient segmentationalgorithm provides robust performance for segmenting the wound area, whereas the entropy filter segmentation algorithm is unstable for this purpose under certain circumstances. Additionally, the best-suited compression strategy is observed to be dependent on (i) the segmentation algorithm used and (ii) the actual data sequence being processed. Full article

(This article belongs to the Special Issue Computational Science and Its Applications 2022)

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