Computers

21 pages, 432 KiB

Open AccessArticle

Application of Proximal Policy Optimization for Resource Orchestration in Serverless Edge Computing

by Mauro Femminella and Gianluca Reali

Computers 2024, 13(9), 224; https://doi.org/10.3390/computers13090224 - 6 Sep 2024

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced [...] Read more.

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced latency and efficient resource usage, both typical requirements of edge-hosted services. However, a badly configured scaling function can introduce unexpected latency due to so-called "cold start" events or service request losses. In this work, we focus on the optimization of resource-based autoscaling on OpenFaaS, the most-adopted open-source Kubernetes-based serverless platform, leveraging real-world serverless traffic traces. We resort to the reinforcement learning algorithm named Proximal Policy Optimization to dynamically configure the value of the Kubernetes Horizontal Pod Autoscaler, trained on real traffic. This was accomplished via a state space model able to take into account resource consumption, performance values, and time of day. In addition, the reward function definition promotes Service-Level Agreement (SLA) compliance. We evaluate the proposed agent, comparing its performance in terms of average latency, CPU usage, memory usage, and loss percentage with respect to the baseline system. The experimental results show the benefits provided by the proposed agent, obtaining a service time within the SLA while limiting resource consumption and service loss. Full article

(This article belongs to the Special Issue Advances in High-Performance Switching and Routing)

30 pages, 5636 KiB

Open AccessReview

A Survey of Blockchain Applicability, Challenges, and Key Threats

by Catalin Daniel Morar and Daniela Elena Popescu

Computers 2024, 13(9), 223; https://doi.org/10.3390/computers13090223 - 6 Sep 2024

Abstract

With its decentralized, immutable, and consensus-based validation features, blockchain technology has grown from early financial applications to a variety of different sectors. This paper aims to outline various applications of the blockchain, and systematically identify general challenges and key threats regarding its adoption. [...] Read more.

With its decentralized, immutable, and consensus-based validation features, blockchain technology has grown from early financial applications to a variety of different sectors. This paper aims to outline various applications of the blockchain, and systematically identify general challenges and key threats regarding its adoption. The challenges are organized into even broader groups, to allow a clear overview and identification of interconnected issues. Potential solutions are introduced into the discussion, addressing their possible ways of mitigating these challenges and their forward-looking effects in fostering the adoption of blockchain technology. The paper also highlights some potential directions for future research that may overcome these challenges to unlock further applications. More generally, the article attempts to describe the potential transformational implications of blockchain technology, through the manner in which it may contribute to the advancement of a diversity of industries. Full article

(This article belongs to the Special Issue Next Generation Blockchain, Information Security and Soft Computing for Future IoT Networks)

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24 pages, 1191 KiB

Open AccessArticle

Usability Heuristics for Metaverse

by Khalil Omar, Hussam Fakhouri, Jamal Zraqou and Jorge Marx Gómez

Computers 2024, 13(9), 222; https://doi.org/10.3390/computers13090222 - 6 Sep 2024

Abstract

The inclusion of usability heuristics into the metaverse is aimed at solving the unique issues raised by virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. This research points out the usability challenges of metaverse user interfaces (UIs), such as information [...] Read more.

The inclusion of usability heuristics into the metaverse is aimed at solving the unique issues raised by virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. This research points out the usability challenges of metaverse user interfaces (UIs), such as information overloading, complex navigation, and the need for intuitive control mechanisms in these immersive spaces. By adapting the existing usability models to suit the metaverse context, this study presents a detailed list of heuristics and sub-heuristics that are designed to improve the overall usability of metaverse UIs. These heuristics are essential when it comes to creating user-friendly, inclusive, and captivating virtual environments (VEs) that take care of the needs of three-dimensional interactions, social dynamics demands, and integration with digital–physical worlds. It should be noted that these heuristics have to keep up with new technological advancements, as well as changing expectations from users, hence ensuring a positive user experience (UX) within the metaverse. Full article

(This article belongs to the Special Issue Gamification and Serious Games Applications in Immersive Learning Environments)

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29 pages, 1466 KiB

Open AccessArticle

Teach Programming Using Task-Driven Case Studies: Pedagogical Approach, Guidelines, and Implementation

by Jaroslav Porubän, Milan Nosál’, Matúš Sulír and Sergej Chodarev

Computers 2024, 13(9), 221; https://doi.org/10.3390/computers13090221 - 5 Sep 2024

Viewed by 169

Abstract

Despite the effort invested to improve the teaching of programming, students often face problems with understanding its principles when using traditional learning approaches. This paper presents a novel teaching method for programming, combining the task-driven methodology and the case study approach. This method [...] Read more.

Despite the effort invested to improve the teaching of programming, students often face problems with understanding its principles when using traditional learning approaches. This paper presents a novel teaching method for programming, combining the task-driven methodology and the case study approach. This method is called a task-driven case study. The case study aspect should provide a real-world context for the examples used to explain the required knowledge. The tasks guide students during the course to ensure that they will not fall into bad practices. We provide reasoning for using the combination of these two methodologies and define the essential properties of our method. Using a specific example of the Minesweeper case study from the Java technologies course, the readers are guided through the process of the case study selection, solution implementation, study guide writing, and course execution. The teachers’ and students’ experiences with this approach, including its advantages and potential drawbacks, are also summarized. Full article

(This article belongs to the Special Issue Future Trends in Computer Programming Education)

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21 pages, 3534 KiB

Open AccessArticle

Digital Genome and Self-Regulating Distributed Software Applications with Associative Memory and Event-Driven History

by Rao Mikkilineni, W. Patrick Kelly and Gideon Crawley

Computers 2024, 13(9), 220; https://doi.org/10.3390/computers13090220 - 5 Sep 2024

Viewed by 203

Abstract

Biological systems have a unique ability inherited through their genome. It allows them to build, operate, and manage a society of cells with complex organizational structures, where autonomous components execute specific tasks and collaborate in groups to fulfill systemic goals with shared knowledge. [...] Read more.

Biological systems have a unique ability inherited through their genome. It allows them to build, operate, and manage a society of cells with complex organizational structures, where autonomous components execute specific tasks and collaborate in groups to fulfill systemic goals with shared knowledge. The system receives information from various senses, makes sense of what is being observed, and acts using its experience while the observations are still in progress. We use the General Theory of Information (GTI) to implement a digital genome, specifying the operational processes that design, deploy, operate, and manage a cloud-agnostic distributed application that is independent of IaaS and PaaS infrastructure, which provides the resources required to execute the software components. The digital genome specifies the functional and non-functional requirements that define the goals and best-practice policies to evolve the system using associative memory and event-driven interaction history to maintain stability and safety while achieving the system’s objectives. We demonstrate a structural machine, cognizing oracles, and knowledge structures derived from GTI used for designing, deploying, operating, and managing a distributed video streaming application with autopoietic self-regulation that maintains structural stability and communication among distributed components with shared knowledge while maintaining expected behaviors dictated by functional requirements. Full article

(This article belongs to the Special Issue Software Engineering Methodologies and Languages for Event Driven and Large-Scale Management Systems (SLEMS))

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19 pages, 675 KiB

Open AccessReview

Predicting Student Performance in Introductory Programming Courses

by João P. J. Pires, Fernanda Brito Correia, Anabela Gomes, Ana Rosa Borges and Jorge Bernardino

Computers 2024, 13(9), 219; https://doi.org/10.3390/computers13090219 - 5 Sep 2024

Viewed by 157

Abstract

The importance of accurately predicting student performance in education, especially in the challenging curricular unit of Introductory Programming, cannot be overstated. As institutions struggle with high failure rates and look for solutions to improve the learning experience, the need for effective prediction methods [...] Read more.

The importance of accurately predicting student performance in education, especially in the challenging curricular unit of Introductory Programming, cannot be overstated. As institutions struggle with high failure rates and look for solutions to improve the learning experience, the need for effective prediction methods becomes critical. This study aims to conduct a systematic review of the literature on methods for predicting student performance in higher education, specifically in Introductory Programming, focusing on machine learning algorithms. Through this study, we not only present different applicable algorithms but also evaluate their performance, using identified metrics and considering the applicability in the educational context, specifically in higher education and in Introductory Programming. The results obtained through this study allowed us to identify trends in the literature, such as which machine learning algorithms were most applied in the context of predicting students’ performance in Introductory Programming in higher education, as well as which evaluation metrics and datasets are usually used. Full article

(This article belongs to the Special Issue Future Trends in Computer Programming Education)

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18 pages, 5905 KiB

Open AccessArticle

Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

by János Hollósi, Áron Ballagi, Gábor Kovács, Szabolcs Fischer and Viktor Nagy

Computers 2024, 13(9), 218; https://doi.org/10.3390/computers13090218 - 3 Sep 2024

Viewed by 252

Abstract

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific [...] Read more.

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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21 pages, 3689 KiB

Open AccessArticle

Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of Helicobacter pylori Infection

by Sultan Noman Qasem

Computers 2024, 13(9), 217; https://doi.org/10.3390/computers13090217 - 2 Sep 2024

Viewed by 334

Abstract

The Gram-negative bacterium Helicobacter pylori (H. infection) infects the human stomach and is a major cause of gastritis, peptic ulcers, and gastric cancer. With over 50% of the global population affected, early and accurate diagnosis of H. infection infection is crucial for effective [...] Read more.

The Gram-negative bacterium Helicobacter pylori (H. infection) infects the human stomach and is a major cause of gastritis, peptic ulcers, and gastric cancer. With over 50% of the global population affected, early and accurate diagnosis of H. infection infection is crucial for effective treatment and prevention of severe complications. Traditional diagnostic methods, such as endoscopy with biopsy, serology, urea breath tests, and stool antigen tests, are often invasive, costly, and can lack precision. Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. This research aims to develop and evaluate HeliEns, a novel quantum hybrid ensemble learning algorithm designed for the early and accurate diagnosis of H. infection infection. HeliEns combines the strengths of multiple quantum machine learning models, specifically Quantum K-Nearest Neighbors (QKNN), Quantum Naive Bayes (QNB), and Quantum Logistic Regression (QLR), to enhance diagnostic accuracy and reliability. The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. Individual models (QKNN, QNB, and QLR) were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model was then constructed by integrating these quantum models using a hybrid approach that leverages their diverse strengths. The HeliEns model demonstrated superior performance compared to individual models, achieving an accuracy of 94%, precision of 97%, recall of 92%, and an F1-score of 94% in detecting H. infection infection. The quantum ensemble approach effectively mitigated the limitations of individual models, providing a robust and reliable diagnostic tool. HeliEns significantly improved diagnostic accuracy and reliability for early H. infection detection. The integration of multiple quantum ML algorithms within the HeliEns framework enhanced overall model performance. The non-invasive nature of the HeliEns model offers a cost-effective and user-friendly alternative to traditional diagnostic methods. This research underscores the transformative potential of quantum machine learning in healthcare, particularly in enhancing diagnostic efficiency and patient outcomes. HeliEns represents a significant advancement in the early diagnosis of H. infection infection, leveraging quantum machine learning to provide a non-invasive, accurate, and reliable diagnostic tool. This research highlights the importance of QML-driven solutions in healthcare and sets the stage for future research to further refine and validate the HeliEns model in real-world clinical settings. Full article

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15 pages, 2504 KiB

Open AccessArticle

Research on Identification of Critical Quality Features of Machining Processes Based on Complex Networks and Entropy-CRITIC Methods

by Dongyue Qu, Wenchao Liang, Yuting Zhang, Chaoyun Gu, Guangyu Zhou and Yong Zhan

Computers 2024, 13(9), 216; https://doi.org/10.3390/computers13090216 - 30 Aug 2024

Viewed by 364

Abstract

Aiming at the difficulty in effectively identifying critical quality features in the complex machining process, this paper proposes a critical quality feature recognition method based on a machining process network. Firstly, the machining process network model is constructed based on the complex network [...] Read more.

Aiming at the difficulty in effectively identifying critical quality features in the complex machining process, this paper proposes a critical quality feature recognition method based on a machining process network. Firstly, the machining process network model is constructed based on the complex network theory. The LeaderRank algorithm is used to identify the critical processes in the machining process. Secondly, the Entropy-CRITIC method is used to calculate the weight of the quality features of the critical processes, and the critical quality features of the critical processes are determined according to weight ranking results. Finally, the feasibility and effectiveness of the method are verified by taking the medium-speed marine diesel engine coupling rod machining as an example. The results show that the method can still effectively identify the critical quality features in the case of small sample data and provide support for machining process optimization and quality control, thus improving product consistency, reliability, and machining efficiency. Full article

(This article belongs to the Topic Innovation, Communication and Engineering)

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14 pages, 1893 KiB

Open AccessArticle

A Study of a Drawing Exactness Assessment Method Using Localized Normalized Cross-Correlations in a Portrait Drawing Learning Assistant System

by Yue Zhang, Zitong Kong, Nobuo Funabiki and Chen-Chien Hsu

Computers 2024, 13(9), 215; https://doi.org/10.3390/computers13090215 - 23 Aug 2024

Viewed by 305

Abstract

Nowadays, portrait drawing has gained significance in cultivating painting skills and human sentiments. In practice, novices often struggle with this art form without proper guidance from professionals, since they lack understanding of the proportions and structures of facial features. To solve this limitation, [...] Read more.

Nowadays, portrait drawing has gained significance in cultivating painting skills and human sentiments. In practice, novices often struggle with this art form without proper guidance from professionals, since they lack understanding of the proportions and structures of facial features. To solve this limitation, we have developed a Portrait Drawing Learning Assistant System (PDLAS) to assist novices in learning portrait drawing. The PDLAS provides auxiliary lines as references for facial features that are extracted by applying OpenPose and OpenCV libraries to a face photo image of the target. A learner can draw a portrait on an iPad using drawing software where the auxiliary lines appear on a different layer to the portrait. However, in the current implementation, the PDLAS does not offer a function to assess the exactness of the drawing result for feedback to the learner. In this paper, we present a drawing exactness assessment method using a Localized Normalized Cross-Correlation (NCC) algorithm in the PDLAS. NCC gives a similarity score between the original face photo and drawing result images by calculating the correlation of the brightness distributions. For precise feedback, the method calculates the NCC for each face component by extracting the bounding box. In addition, in this paper, we improve the auxiliary lines for the nose. For evaluations, we asked students at Okayama University, Japan, to draw portraits using the PDLAS, and applied the proposed method to their drawing results, where the application results validated the effectiveness by suggesting improvements in drawing components. The system usability was also confirmed through a questionnaire with a SUS score. The main finding of this research is that the implementation of the NCC algorithm within the PDLAS significantly enhances the accuracy of novice portrait drawings by providing detailed feedback on specific facial features, proving the system’s efficacy in art education and training. Full article

(This article belongs to the Special Issue Smart Learning Environments)

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Computers, Volume 13, Issue 9 (September 2024) – 10 articles

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