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Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern e-commerce systems are more likely focused on mechanisms of security, such as secure transactional protocols, cryptographic schemes and parameter sanitization, and it is assumed that putting these in place will guarantee a secure e-commerce application. However, vulnerabilities in the business application logic itself are often ignored which can make the effect of these security mechanisms null and void. Essentially, the weakest link can be at the server rather than client because of business logic and insecure server-side business components, its security ignoring is another factor, which is done at developer's peril. This paper focuses on the weakest link (component's logic subversion) in the e-commerce system. We outline a logical attack (subversion attack, class Design Flaw) that would not be prevented by the deployment of the mechanisms commonly used in e-commerce systems. To further investigate this problem, we propose a security assurance methodology for service component-oriented application that will be practiced through threat modeling and component fault detection model with further modeling component and its application using unified modeling language secure-design approach with a valid technique (verification, validation model for security-by-design testing) for design flaw detection to avoid the business logic problem in component-based e-commerce applications from existing application logic. [ABSTRACT FROM AUTHOR]

Microservice architecture (MSA) is a mainstream architectural style due to its high maintainability and scalability. In practice, an appropriate microservice‐oriented decomposition is the foundation to make a system enjoy the benefits of MSA. In terms of decomposing monolithic systems into microservices, researchers have been exploring many optimization objectives, of which modularity is a predominantly focused quality attribute. Security is also a critical quality attribute, that measures the extent to which a system protects data from malicious access or use by attackers. Considering security in microservices‐oriented decomposition can help avoid the risk of leaking critical data and other unexpected software security issues. However, few researchers consider the security objective during microservice‐oriented decomposition, because the measurement of security and the trade‐off with other objectives are challenging in reality. To bridge this research gap, we propose a security‐optimized approach for microservice‐oriented decomposition (So4MoD). In this approach, we adapt five metrics from previous studies for the measurement of the data security of candidate microservices. A multi‐objective optimization algorithm based on NSGA‐II is designed to search for microservices with optimized security and modularity. To validate the effectiveness of the proposed So4MoD, we perform several experiments on eight open‐source projects and compare the decomposition results to other three state‐of‐the‐art approaches, that is, FoSCI, CO‐GCN, and MSExtractor. The experiment results show that our approach can achieve at least an 11.5% improvement in terms of security metrics. Moreover, the decomposition results of So4MoD outperform other approaches in four modularity metrics, demonstrating that So4MoD can optimize data security while pursuing a well‐modularized MSA. [ABSTRACT FROM AUTHOR]

The service-oriented heterogeneous cloud computing system offers paid computing services through its powerful processors. However, task execution failures caused by processors can diminish application reliability and user service quality. Replication technology can enhance application reliability, but an inappropriate number of replicas may occupy valuable computing resources. To address this, we propose FastMinRR, a fault-tolerant scheduling algorithm that rapidly minimizes the number of replicas while meeting reliability requirements. FastMinRR initially allocates all tasks to the currently available and most reliable processor without replication, utilizing a priority queue to store tasks based on increasing reliability. Subsequently, FastMinRR dequeues a task, increases its replicas, updates reliability, recalculates application reliability, and enqueues the task. This process repeats until the application reliability aligns with requirements. Experiments demonstrate that, compared to existing fault-tolerant scheduling algorithms in service-oriented heterogeneous cloud computing systems, FastMinRR efficiently increases the minimum number of task replicas and meets application reliability requirements. [ABSTRACT FROM AUTHOR]

Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality. [ABSTRACT FROM AUTHOR]

The arising of the Cyber–Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mathematics, Mechanics & Computer Science is the property of Al-Farabi Kazakh National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fault prediction in service-oriented architecture-based models has been recognized as one of the essential processes to reduce computational expenses and computational complexities of software system improvement. Prediction of errors and determining their positions at an early stage facilitate maintenance mechanisms very easily and enhance resource utilities. Hence, an effective fault prediction mechanism named fractional water cycle algorithm (Fr-WCA) is established in this research. The web service description language data are converted into JAVA files, and faults are injected into it in order to create a database. Thereafter, features like object-oriented metrics and statistical features are extracted, and data augmentation is completed effectively. The process of fault prediction is executed using a deep Q network classifier, which is trained by means of Fr-WCA. The Fr-WCA is the combination of fractional calculus and water cycle algorithm. The Fr-WCA achieved the greatest precision, recall, and f-measure of 0.932, 0.956, and 0.947. [ABSTRACT FROM AUTHOR]

The study sought to develop and validate an innovative leadership model for improving service delivery in urban local authorities. The prevalence of poor service delivery in urban local authorities motivated this study. The selected constructs of innovative leadership are innovative and creative thinking behaviour, risk taking and change oriented leadership, stakeholder participation, public administration core values, and service leadership. Data was collected using a survey approach from residents of Harare City Council, Zimbabwe. A structured questionnaire was used to collect 415 validated responses which were analysed using AMOS structural equation modelling (SEM) application. The innovative leadership model was confirmed and the study concluded that innovative and creative thinking behaviour, risk taking and change oriented leadership stakeholder participation, service leadership, and public administration core values, as constructs of innovative leadership, can go a long way on improving service delivery challenges in urban local authorities. As a result, the study validated and concluded the role of the constructs on service delivery challenges in urban local authorities. The study therefore recommends the adoption of the tested model as a plausible solution to service delivery challenges being faced by urban local authorities. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) has revolutionized manufacturing, necessitating an examination of its sustainability implications, particularly for Small and Medium Enterprises (SMEs). Consequently, this study aims to investigate how I4.0 technologies can assist SMEs in achieving sustainability. A systematic literature review (SLR) was conducted to achieve this goal, utilizing articles from the Web of Science and Scopus databases. Following a structured methodology, 42 articles were selected based on predefined criteria. In the first stage of the SLR, R® software was utilized to analyze the journals, countries, and article growth. Subsequently, with the assistance of Mendeley®, the selected articles were analyzed to identify correlations among I4.0, sustainability, and SMEs. This process identified 17 sustainability functions supported by I4.0 in SMEs, including Employee skill development, Controlling Waste Generation, and Organizing Production Processes. Furthermore, the results demonstrate how I4.0 technologies can assist SMEs in achieving each sustainability function. The most suggested technologies include the Internet of Things (IoT), Cloud Computing, Big Data Analytics, and Artificial Intelligence, serving as the foundation for specific applications across the three dimensions of sustainability. This article contributes to theory by synthesizing previous knowledge of three approaches that had not been studied before and identifying research gaps for future work, such as the correlation between I4.0 and sustainability in service-oriented SMEs and studying the implementation of Industry 5.0 in SMEs. Finally, this research will guide manufacturers, industrialists, and technology suppliers in designing and implementing technologies to facilitate I4.0 for sustainable development in SMEs. • Literature on Industry 4.0 and sustainability overlooks SME characteristics. • Interest in sustainability and Industry 4.0 is growing among SMEs. • Industry 4.0 technologies can boost sustainability in Small and Medium Enterprises. • Simplicity in implementing Industry 4.0 technologies is vital for SMEs. • Practical applications of how technologies can address both sustainability and SMEs. [ABSTRACT FROM AUTHOR]

Current research in Circular Economy (CE) fails to address the occurrence of Rebound Effects (RE), which are systemic and behavioural responses to the implementation of interventions hindering the potential sustainability benefits. This paper aims to advance the academic discussion and the practical consideration of RE by exploring the potential of using System Dynamics (SD) to enable the ex-ante identification of potential RE of CE initiatives. A five-stage simulation-based approach for the identification and mitigation of potential rebound effects (AIMRE) is proposed. Its application is demonstrated in a use-oriented product/service system (PSS) case focused on a high-end dress rental service. The AIMRE enables the representation of the magnitude and reasons for RE occurrence through 14 scenarios. The finding highlights the importance of considering the interplay between consumers' and companies' decision-making processes in quantifying, understanding, and mitigating RE occurrence. • Simulation-based approach to investigate rebound effects (RE). • Helps identify potential RE in Circular Economy (CE) initiatives. • Relies on relevant consumers' and companies' decision-making structures. • Enables quantification through a valid RE magnitude quantification structure. • Scenarios of CE adoption provide insight into the reason for RE occurrence. [ABSTRACT FROM AUTHOR]

Educational big data analysis is facilitated by the significant amount of unstructured data found in education institutions. Python has various toolkits for both structured and unstructured data processing. However, its ability for processing large-scale data is limited. On the other hand, Spark is a big data processing framework, but it does not have the needed toolkits for processing unstructured rich text documents, 3D model and image processing. In this study, we develop a generic framework that integrates Python toolkits and Spark based on service-oriented architecture. The framework automatically extends the serial algorithm written in Python to distributed algorithm to accomplish parallel processing tasks seamlessly. First, our focus is on achieving non-intrusive deployment to Spark servers and how to run Python codes in Spark environment to process rich text documents. Second, we propose a compression-based schema to address the poor performance of small sized files in HDFS. Finally, we design a generic model that can process different types of poly-structured data such as 3D models and images. We published the services used in the system for sharing them at https level for constructing different systems. It is evaluated through simulation experiments using large-scale rich text documents, 3D models and images. According to the results, the speedup is 49 times faster than the standalone Python-docx in the simulations of extracting 232 GB docx files when eight physical nodes with 128 cores are used. It reaches about 89 times after further compression schema is applied. In addition, simulations for 3D model descriptors' extraction show that the simulation achieves a speedup of about 116 times. In the large-scale image's HOG features extraction simulation task of up to 256.7 GB (6,861,024 images), a speedup of up to 110 times is achieved. [ABSTRACT FROM AUTHOR]

• Whether a manufacturing enterprise should improve service performance through AI technology and how to sell products and AI services. • Summarize the service-oriented transformation path through case studies of three automotive manufacturing enterprises. • Manufacturers should not offer AI services when the quality-cost coefficient of services is low. The sales model for products and AI services depends on the quality-cost coefficient and the impact of AI on the service. • The study provides theoretical support and practical basis for the service-oriented transformation path of manufacturing enterprises. In the era of the digital economy, the rapid development and integration of next-generation information technologies, particularly Artificial Intelligence (AI), have fundamentally altered the operational paradigms and business models of the manufacturing sector. The incorporation of AI elements not only technically but also strategically affects various facets of manufacturing, with relevance to service performance. This study analyzed the effect of AI technology on service quality and manufacturers' strategic decision-making using a mathematical model. Our results indicate that manufacturers should focus on the quality-cost coefficient of services and the impact of AI technology on services, regardless of whether they adopt AI. Importantly, manufacturers should focus on the product value, considering services as value-added components. Subsequently, manufacturers should avoid excessively improving service quality as this could make it difficult to profit from over qualified services. Finally, manufacturers lack incentives to provide low-quality services when determining the sales model for products and services. Manufacturers can maximize profits by continually striving to improve service quality. We studied successful AI-driven upgrades and proposed practical transformation modes as references. This study provides an important reference for guiding AI-driven service innovation practices in manufacturers. [ABSTRACT FROM AUTHOR]

Service Oriented Architecture (SOA) is a methodology that promotes cooperation between services with diverse, but connected functions. Web Service technology paved the way for microservice architecture as it is a feature of modern web applications that resulted from the rise of SOA. With the proliferation of self-contained services, the ease of finding has emerged as a critical concern. Due to the increasing number of services that perform identical tasks, it has become difficult for users to select the most feasible service. Providing the most relevant service for the customer quickly is a crucial infrastructure task, and undiscovered services increase ecosystem expenses. Syntactic, semantic-conscious, and ontology-based studies have been presented as ways to improve the effectiveness and quality of service discovery techniques. While there are many approaches that have been proposed and validated for service discovery in literature, these studies are fragmented and there is a lack of overview of the techniques of web service discovery. As such, we conduct a Systematic Literature Review (SLR) study to review the existing body of knowledge surrounding service discovery and discuss the state-of-the-art. We present an overview of the techniques and empirical evidence by identifying, analyzing, and classifying the papers. Among the 764 papers we retrieved, 54 papers were included. We provide a comprehensive analysis of methodologies and tools for discovering web services. • We conducted an SLR study to review the service discovery. • We present an overview of the techniques and empirical evidence. • 54 papers were investigated in detail. • We provide a comprehensive analysis of methodologies and tools for discovering web services. [ABSTRACT FROM AUTHOR]