Research

25 pages, 46929 KiB

Open AccessArticle

Impact of Image Compression on In Vitro Cell Migration Analysis

by Ehsaneddin Jalilian, Michael Linortner and Andreas Uhl

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

Viewed by 1330

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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25 pages, 652 KiB

Open AccessArticle

The Impact of COVID-19 on Purchase Behavior Changes in Smart Regions

by Mária Pomffyová and Lenka Veselovská

Computers 2023, 12(2), 38; https://doi.org/10.3390/computers12020038 - 11 Feb 2023

Cited by 1 | Viewed by 2294

Abstract

The COVID-19 pandemic has changed consumer behavior due to various restrictions and increased degrees of ICT use. By establishing and verifying the validity of the hypotheses, we aim to compare intensities of mutual correlations that indicate changes in consumer behavior depending on the [...] Read more.

The COVID-19 pandemic has changed consumer behavior due to various restrictions and increased degrees of ICT use. By establishing and verifying the validity of the hypotheses, we aim to compare intensities of mutual correlations that indicate changes in consumer behavior depending on the degree and nature of changes in selected socio-demographic or socio-economic factors. The statistical evaluation of the answers obtained in surveys of representative samples of 987 respondents from the Slovak Republic (implemented in 2021 about the dual quality of goods sold in the EU) and also the answers of 347 respondents (in 2022 aimed at changes in Slovak consumer behavior) will be carried out with multivariate analyses using the SPSS program. The outputs indicated that during self-isolation periods, Slovak consumers bought more or the same amount as before the pandemic; shopping habits were mainly changed by women and groups with lower household income. Test subjects preferred the quality products and products posing the least amount of risk to health. All consumers intend to continue to shop through e-commerce platforms where they prefer a more personal experience (through social media or YouTube). Low-income people’s budgets are threatened by cheap products and poor distribution of spending, especially among young people. We recommend simplifying personalized visualized sales and education content and e-methods of information sharing also in order to make them accessible to digitally disadvantaged groups (according to income, age, education, etc.). The use of blockchains increases transparency of production and sales value chains, reducing the occurrence of unfair practices, and promoting participatory public dialogue. Full article

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

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21 pages, 6354 KiB

Open AccessArticle

Monkeypox Outbreak Analysis: An Extensive Study Using Machine Learning Models and Time Series Analysis

by Ishaani Priyadarshini, Pinaki Mohanty, Raghvendra Kumar and David Taniar

Computers 2023, 12(2), 36; https://doi.org/10.3390/computers12020036 - 07 Feb 2023

Cited by 8 | Viewed by 2997

Abstract

The sudden unexpected rise in monkeypox cases worldwide has become an increasing concern. The zoonotic disease characterized by smallpox-like symptoms has already spread to nearly twenty countries and several continents and is labeled a potential pandemic by experts. monkeypox infections do not have [...] Read more.

The sudden unexpected rise in monkeypox cases worldwide has become an increasing concern. The zoonotic disease characterized by smallpox-like symptoms has already spread to nearly twenty countries and several continents and is labeled a potential pandemic by experts. monkeypox infections do not have specific treatments. However, since smallpox viruses are similar to monkeypox viruses administering antiviral drugs and vaccines against smallpox could be used to prevent and treat monkeypox. Since the disease is becoming a global concern, it is necessary to analyze its impact and population health. Analyzing key outcomes, such as the number of people infected, deaths, medical visits, hospitalizations, etc., could play a significant role in preventing the spread. In this study, we analyze the spread of the monkeypox virus across different countries using machine learning techniques such as linear regression (LR), decision trees (DT), random forests (RF), elastic net regression (EN), artificial neural networks (ANN), and convolutional neural networks (CNN). Our study shows that CNNs perform the best, and the performance of these models is evaluated using statistical parameters such as mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and R-squared error (R2). The study also presents a time-series-based analysis using autoregressive integrated moving averages (ARIMA) and seasonal auto-regressive integrated moving averages (SARIMA) models for measuring the events over time. Comprehending the spread can lead to understanding the risk, which may be used to prevent further spread and may enable timely and effective treatment. Full article

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

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21 pages, 1592 KiB

Open AccessArticle

Cognitive Impairment and Dementia Data Model: Quality Evaluation and Improvements

by Dessislava Petrova-Antonova and Sophia Lazarova

Computers 2023, 12(2), 29; https://doi.org/10.3390/computers12020029 - 30 Jan 2023

Viewed by 1480

Abstract

Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from different repositories have to be integrated. Since each data [...] Read more.

Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from different repositories have to be integrated. Since each data source keeps entities with different names and relationships at different levels of granularity and format, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. Thus, in our previous work, we proposed a hierarchical cognitive impairment and dementia data model that keeps the semantics of the data in a human-readable format and accelerates the interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. This paper extends our previous work by evaluating and improving the data model by adapting the methodology proposed by D. Moody and G. Shanks. The completeness, simplicity, correctness and integrity of the data model are assessed and based on the results a new, improved version of the model is generated. The understandability of the improved model is evaluated using an online questionnaire. Simplicity and integrity are also considered as well as the factors that may influence the flexibility of the data model. Full article

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

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18 pages, 2076 KiB

Open AccessArticle

Experiments and Evaluation of a Container Migration Data-Auditing System on Edge Computing Environment

by Toshihiro Uchibayashi, Bernady Apduhan, Takuo Suganuma and Masahiro Hiji

Computers 2023, 12(2), 27; https://doi.org/10.3390/computers12020027 - 27 Jan 2023

Cited by 1 | Viewed by 1437

Abstract

With the proliferation of IoT sensors and devices, storing collected data in the cloud has become common. A wide variety of data with different purposes and forms are not directly stored in the cloud but are sent to the cloud via edge servers. [...] Read more.

With the proliferation of IoT sensors and devices, storing collected data in the cloud has become common. A wide variety of data with different purposes and forms are not directly stored in the cloud but are sent to the cloud via edge servers. At the edge server, applications are running in containers and virtual machines to collect data. However, the current deployment and movement mechanisms for containers and virtual machines do not consider any conventions or regulations for the applications and the data it contains. Therefore, it is easy to deploy and migrate containers and virtual machines. However, the problem arises when it is deployed or migrated, which may violate the licensing terms of the contained applications, the rules of the organization, or the laws and regulations of the concerned country. We have already proposed a data-audit control mechanism for the migration of virtual machines. The proposed mechanism successfully controls the unintentional and malicious migration of virtual machines. We expect similar problems with containers to occur as the number of edge servers increases. Therefore, we propose a policy-based data-audit control system for container migration. The proposed system was verified in the implemented edge computing environment and the results showed that adding the proposed data-audit control mechanism had a minimal impact on migration time and that the system was practical enough. In the future, we intend to conduct verification not in a very compact and short-range environment such as this one but on an existing wide-area network. Full article

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

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26 pages, 2681 KiB

Open AccessArticle

Capacitated Waste Collection Problem Solution Using an Open-Source Tool

by Adriano Santos Silva, Filipe Alves, José Luis Diaz de Tuesta, Ana Maria A. C. Rocha, Ana I. Pereira, Adrián M. T. Silva and Helder T. Gomes

Computers 2023, 12(1), 15; https://doi.org/10.3390/computers12010015 - 07 Jan 2023

Cited by 3 | Viewed by 1892

Abstract

Population in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried [...] Read more.

Population in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems. Full article

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

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23 pages, 465 KiB

Open AccessArticle

Experiments with Active-Set LP Algorithms Allowing Basis Deficiency

by Pablo Guerrero-García and Eligius M. T. Hendrix

Computers 2023, 12(1), 3; https://doi.org/10.3390/computers12010003 - 23 Dec 2022

Viewed by 1193

Abstract

An interesting question for linear programming (LP) algorithms is how to deal with solutions in which the number of nonzero variables is less than the number of rows of the matrix in standard form. An approach is that of basis deficiency-allowing (BDA) simplex [...] Read more.

An interesting question for linear programming (LP) algorithms is how to deal with solutions in which the number of nonzero variables is less than the number of rows of the matrix in standard form. An approach is that of basis deficiency-allowing (BDA) simplex variations, which work with a subset of independent columns of the coefficient matrix in standard form, wherein the basis is not necessarily represented by a square matrix. We describe one such algorithm with several variants. The research question deals with studying the computational behaviour by using small, extreme cases. For these instances, we must wonder which parameter setting or variants are more appropriate. We compare the setting of two nonsimplex active-set methods with Holmström’s TomLab LpSimplex v3.0 commercial sparse primal simplex commercial implementation. All of them update a sparse QR factorization in Matlab. The first two implementations require fewer iterations and provide better solution quality and running time. Full article

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

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41 pages, 742 KiB

Open AccessArticle

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

by Gizem Akman, Philip Ginzboorg, Mohamed Taoufiq Damir and Valtteri Niemi

Computers 2023, 12(1), 2; https://doi.org/10.3390/computers12010002 - 20 Dec 2022

Cited by 1 | Viewed by 2291

Abstract

Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. [...] Read more.

Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. We consider how to provide secure and privacy-preserving communication channels between a mobile user and a MEC application in the non-roaming case. It includes protocols for registration of the user to the main server of the MEC application, renewal of the shared key, and usage of the MEC application in the MEC host when the user is stationary or mobile. For these protocols, we designed a privacy-enhanced version of the 5G authentication and key management for applications (AKMA) service. We formally verified the current specification of AKMA using ProVerif and found a new spoofing attack as well as other security and privacy vulnerabilities. Then we propose a fix against the spoofing attack. The privacy-enhanced AKMA is designed considering these shortcomings. We formally verified the privacy-enhanced AKMA and adapted it to our solution. Full article

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

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35 pages, 9144 KiB

Open AccessArticle

A Survey on Security Attacks and Intrusion Detection Mechanisms in Named Data Networking

by Abdelhak Hidouri, Nasreddine Hajlaoui, Haifa Touati, Mohamed Hadded and Paul Muhlethaler

Computers 2022, 11(12), 186; https://doi.org/10.3390/computers11120186 - 14 Dec 2022

Cited by 9 | Viewed by 2403

Abstract

Despite the highly secure content sharing and the optimized forwarding mechanism, the content delivery in a Named Data Network (NDN) still suffers from numerous vulnerabilities that can be exploited to reduce the efficiency of such architecture. Malicious attacks in NDN have become more [...] Read more.

Despite the highly secure content sharing and the optimized forwarding mechanism, the content delivery in a Named Data Network (NDN) still suffers from numerous vulnerabilities that can be exploited to reduce the efficiency of such architecture. Malicious attacks in NDN have become more sophisticated and the foremost challenge is to identify unknown and obfuscated malware, as the malware authors use different evasion techniques for information concealing to prevent detection by an Intrusion Detection System (IDS). For the most part, NDN faces immense negative impacts from attacks such as Cache Pollution Attacks (CPA), Cache Privacy Attacks, Cache Poisoning Attacks, and Interest Flooding Attacks (IFA), that target different security components, including availability, integrity, and confidentiality. This poses a critical challenge to the design of IDS in NDN. This paper provides the latest taxonomy, together with a review of the significant research works on IDSs up to the present time, and a classification of the proposed systems according to the taxonomy. It provides a structured and comprehensive overview of the existing IDSs so that a researcher can create an even better mechanism for the previously mentioned attacks. This paper discusses the limits of the techniques applied to design IDSs with recent findings that can be further exploited in order to optimize those detection and mitigation mechanisms. Full article

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

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14 pages, 872 KiB

Open AccessArticle

Challenges of IoT Identification and Multi-Level Protection in Integrated Data Transmission Networks Based on 5G/6G Technologies

by Gennady Dik, Alexander Bogdanov, Nadezhda Shchegoleva, Aleksandr Dik and Jasur Kiyamov

Computers 2022, 11(12), 178; https://doi.org/10.3390/computers11120178 - 07 Dec 2022

Cited by 2 | Viewed by 1698

Abstract

This paper illustrates the main problematic issues of minimizing technological risks in the construction of an integrated architecture for the protection of a “smart habitat” (SH). We analyze the use of the IoT to identify both object hazards and the categorization of switching [...] Read more.

This paper illustrates the main problematic issues of minimizing technological risks in the construction of an integrated architecture for the protection of a “smart habitat” (SH). We analyze the use of the IoT to identify both object hazards and the categorization of switching detection in information collection and processing centers. The article proposes wired and wireless data-transmission systems for the required level of efficiency as well as SH protection. Particular attention is paid to the organization of multi-level protection of promising 5G/6G cellular networks based on the analysis of the security threat landscape. Full article

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

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12 pages, 2082 KiB

Open AccessArticle

An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage

by Antonio M. Rinaldi, Cristiano Russo and Cristian Tommasino

Computers 2022, 11(12), 172; https://doi.org/10.3390/computers11120172 - 30 Nov 2022

Cited by 2 | Viewed by 1876

Abstract

In the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art [...] Read more.

In the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art exhibits, or generally a city. Moreover, the spread of new and more powerful mobile devices jointly with virtual reality (VR) visors contributes to the spread of AR in cultural heritage. This work presents an augmented reality mobile system based on content-based image analysis techniques and linked open data to improve user knowledge about cultural heritage. In particular, we explore the uses of traditional feature extraction methods and a new way to extract them employing deep learning techniques. Furthermore, we conduct a rigorous experimental analysis to recognize the best method to extract accurate multimedia features for cultural heritage analysis. Eventually, experiments show that our approach achieves good results with respect to different standard measures. Full article

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

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Review

Jump to: Research, Other

45 pages, 8792 KiB

Open AccessReview

A Systematic Review on Social Robots in Public Spaces: Threat Landscape and Attack Surface

by Samson O. Oruma, Mary Sánchez-Gordón, Ricardo Colomo-Palacios, Vasileios Gkioulos and Joakim K. Hansen

Computers 2022, 11(12), 181; https://doi.org/10.3390/computers11120181 - 08 Dec 2022

Cited by 8 | Viewed by 4035

Abstract

There is a growing interest in using social robots in public spaces for indoor and outdoor applications. The threat landscape is an important research area being investigated and debated by various stakeholders. Objectives: This study aims to identify and synthesize empirical research on [...] Read more.

There is a growing interest in using social robots in public spaces for indoor and outdoor applications. The threat landscape is an important research area being investigated and debated by various stakeholders. Objectives: This study aims to identify and synthesize empirical research on the complete threat landscape of social robots in public spaces. Specifically, this paper identifies the potential threat actors, their motives for attacks, vulnerabilities, attack vectors, potential impacts of attacks, possible attack scenarios, and mitigations to these threats. Methods: This systematic literature review follows the guidelines by Kitchenham and Charters. The search was conducted in five digital databases, and 1469 studies were retrieved. This study analyzed 21 studies that satisfied the selection criteria. Results: Main findings reveal four threat categories: cybersecurity, social, physical, and public space. Conclusion: This study completely grasped the complexity of the transdisciplinary problem of social robot security and privacy while accommodating the diversity of stakeholders’ perspectives. Findings give researchers and other stakeholders a comprehensive view by highlighting current developments and new research directions in this field. This study also proposed a taxonomy for threat actors and the threat landscape of social robots in public spaces. Full article

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

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Other

Jump to: Research, Review

29 pages, 2625 KiB

Open AccessProject Report

Development of a Self-diagnostic System Integrated into a Cyber-Physical System

by Domingos F. Oliveira, João P. Gomes, Ricardo B. Pereira, Miguel A. Brito and Ricardo J. Machado

Computers 2022, 11(9), 131; https://doi.org/10.3390/computers11090131 - 29 Aug 2022

Cited by 1 | Viewed by 2054

Abstract

CONTROLAR provides Bosch with an intelligent functional testing machine used to test the correct functioning of the car radios produced. During this process, the radios are submitted to several tests, raising the problem of how the machine detects errors in several radios consecutively, [...] Read more.

CONTROLAR provides Bosch with an intelligent functional testing machine used to test the correct functioning of the car radios produced. During this process, the radios are submitted to several tests, raising the problem of how the machine detects errors in several radios consecutively, making it impossible to know if the device has a problem since it has no module to see if it works correctly. This article arises from the need to find a solution to solve this problem, which was to develop a self-diagnostic system that will ensure the reliability and integrity of the cyber-physical system, passing a detailed state of the art. The development of this system was based on the design of an architecture that combines the KDT methodology with a DSL to manage and configure the tests to integrate the self-diagnostic test system into a CPS. A total of 28 test cases were performed to cover all its functionalities. The results show that all test cases passed. Therefore, the system meets all the proposed objectives. Full article

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

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