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Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar. [ABSTRACT FROM AUTHOR]

Software fault prediction (SFP) is a crucial aspect of software engineering, aiding in the early identification of potential defects. This proactive approach significantly contributes to enhancing software quality and reliability. However, a common challenge in SFP is class imbalance (CI). Ensemble learning (EL) is a powerful strategy for refining SFP models in object‐oriented systems with imbalanced data and improving sensitivity to minority classes. This study aimed to improve the effectiveness of ensemble classes in SFP within object‐oriented systems, tackling the challenges associated with imbalanced data. It focuses on enhancing the performance of three ensemble classifiers, BalancedBagging, RUSBoost, and EasyEnsemble, explicitly designed for imbalanced datasets. In Enhanced_BalancedBagging (E_BB) and ROSBoost, random undersampling (RUS) is substituted with random oversampling (ROS). Meanwhile, Enhanced_EasyEnsemble (E_EE) replaces RUS with ROS and AdaBoost with XGBoost. The experimental results demonstrate the superior performance of E_BB, ROSBoost, and E_EE over their base models, achieving the highest F‐measure, balanced accuracy, and AUC. Statistical tests, such as the Wilcoxon signed‐rank test, provide robust support for the enhanced models, highlighting their practical significance through substantial improvements in F‐measure and AUC, as indicated by low negative rank sums and large effect sizes. [ABSTRACT FROM AUTHOR]

Chatter is a self-induced vibration that results in poor surface quality in the high-speed camshaft grinding process. Accurate and timely chatter prediction is very important for chatter suppression. Consequently, an online chatter prediction method and chatter software system for intelligent manufacturing are urgently required. This paper proposes a novel strategy to predict the chatter for the high-speed grinding process of the camshaft. In this method, the vibration signals acquired by the sensor are decomposed by variational mode decomposition. The backpropagation neural networks model is used to predict grinding chatter. High-speed grinding experiments with different conditions were performed to evaluate the effectiveness of the proposed method. Finally, the vibration state prediction system is developed promptly for chatter prediction. The results show that the proposed method is suitable for chatter prediction in the high-speed grinding process of camshaft, and the correct rate of chatter prediction is 96.4%. [ABSTRACT FROM AUTHOR]

The threat of side‐channel attacks poses a significant risk to the security of cryptographic algorithms. To counter this threat, we have designed an AES system capable of defending against such attacks, supporting AES‐128, AES‐192, and AES‐256 encryption standards. In our system, the CPU oversees the AES hardware via the AHB bus and employs true random number generation to provide secure random inputs for computations. The hardware implementation of the AES S‐box utilizes complex domain inversion techniques, while intermediate data is shielded using full‐time masking. Furthermore, the system incorporates double‐path error detection mechanisms to thwart fault propagation. Our results demonstrate that the system effectively conceals key power information, providing robust resistance against CPA attacks, and is capable of detecting injected faults, thereby mitigating fault‐based attacks. [ABSTRACT FROM AUTHOR]

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any "extensions library" requirement from Agents.jl, which is paramount in many other tools. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

Electronic Accounting (E-accounting) is considered a new concept in the field of accounting. E-accounting replaces paper with electronic sources for accounting records and documentation. It retains a lot of time, zeal, paper and funds for any type of organisation. New software for recording accounting transactions is released every day which is giving technological improvements. E-accounting is characterised as the use of Internet technologies and online applications for business accounting. Additionally, it helps organisations maintain their accounting software and financial data in a secure manner while granting authorised users real-time access regardless of location or computer platform. The objective of the paper is to understand the basic concept of E-accounting, the characteristics of E-accounting, its benefits and drawbacks, and its conclusion. The purpose of this study is to shed light on the implications of E-accounting in modern companies. The review of related literature that was used to develop the study’s theoretical framework served as the secondary source of data for this report. According to surveys, many businesses fail not just due to low-quality products, a lack of qualified employees, or managerial problems, but also because of bad accounting procedures. It has been determined that the company needs to switch to current accounting procedures from outdated ones. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67–77% vs. 80–86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions In 2021, a Technical Meeting was hosted by the International Atomic Energy Agency (IAEA) where it was recommended that a standardized method for assessing the accuracy of film dose calculations should be established.To design an audit that evaluates the accuracy of film dosimetry processes. To propose a framework for identifying out‐of‐tolerance results and to perform an international pilot study to test the audit design.Six members of an international Dosimetry Audit Network (DAN) developed an audit for radiochromic film dosimetry. A single host center provided the materials to each participating DAN member to conduct the audits. Materials included: (1) a set of two irradiated audit films (10 Sq: 10 cm × 10 cm, 15 Sq: 15 cm × 15 cm), (2) a reference calibration film set, and (3) a blank sheet of film. The participants were blinded to the dose and tasked with producing dose maps using their standard film dosimetry process. Average Region‐Of‐Interest (ROI: 2 cm × 2 cm) dose was measured from the dose maps and compared to the known dose. In the audit, all participants used their local scanning and software protocols. Film calibration was performed in two distinct ways: (1) using a calibration film set which was provided by the host center and (2) using a calibration film set which was locally irradiated. Several variations of the audit were also performed to examine how scanning and software processing can affect film dosimetry results. In the first variation of the audit (VariantA), a set of film scans was processed using five different software solutions. In the second variation of the audit (VariantB), all films were scanned on the same scanner and processed using two in‐house software solutions.Taking one film scan from each participant, the standard deviations (1σ) (SD) in the dose returned from the host calibration and returned from the local calibration were ±7.2% and ±6.5% respectively, with variations from −12.4% to 12.9% of the known dose. The larger dose variations in the data set were attributed to the corrections applied for variations in scanner brightness during processing and incorrectly assigned calibration doses. When the raw image data set was processed by an expert user of each software solution (VariantA) the SDs were ±2.7% and ±3.7% for in‐house and vendor‐based software, respectively. When the films were scanned on a single scanner and processed with the two in‐house software solutions (VariantB) the results had a SD of ±2.3%.An audit has been designed and tested for radiotherapy film dosimetry at an international level. A framework for diagnosing issues within a film dosimetry process has been proposed that could be used to audit centers that use film as a dosimeter. Incorporating quality assurance throughout the film process is important in obtaining accurate and consistent film dosimetry. A better understanding of vendor‐based software systems is necessary for users to process accurate and consistent film dosimetry. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

This paper presents the novel design and analysis of a 25 W broadband Class-AB GaN HEMT-based power amplifier working over 0.9–1.7 GHz. A large signal model of the Wolfspeed-make GaN HEMT CG2H40025 has been used in this design. The comprehensive design process has been carried out with the help of the Advanced Design System software by Keysight. Instead of conjugately matching the load and source impedance obtained from load-pull analysis of a nonlinear model of CG2H40025, the value of load and source impedance has been tuned with the help of a novel design approach of an ideal matching design approach. Thus, to achieve an output power of more than 25 W and a flat large signal gain of 15.425 ± 0.375 dB, a novel design approach has been employed to create input and output matching networks, maintaining a lower Q value results in broad bandwidth and for determining the load and source impedances. The circuit has been fabricated on Roger's substrate RO4350B having a dielectric constant of 3.66 and a loss tangent of 0.0031. The unique design approach based on electromagnetic simulation of the circuit at each design step yielded experimental results that agreed very well with the simulated results. The fabricated power amplifier achieves a power-added efficiency of over 54.78% and a drain efficiency of over 55.45% throughout the entire frequency range. It also delivers a maximum output power of 32.18 W at 1.3 GHz. This power amplifier is developed for L-band Foliage Penetrating Radar. [ABSTRACT FROM AUTHOR]

Fluid-flow approximation is an approach to modelling and evaluating the performance of vast computer networks. Due to varying traffic and performance of transmission protocols reacting to traffic overloads, computer networks are in a permanent transient state. The fluid-flow method main advantage is its ability to analyse these transient states. The article reviews and organises several versions of this approach, indicating a few errors. The main reason for these errors is confusion or lack of distinction between the two versions of the Internet Protocol -- when the queue of packets at a node is too long, they may be destroyed or only marked as redundant. The paper compares and evaluates these fluid-flow approximation models with mild and aggressive settings of RED parameters. The authors build a software system with hitherto unprecedented capabilities regarding the size of the networks to be analysed and with innovative way of organising the calculations. The paper shows how large differences imprecise assumptions can introduce in quantitative results. [ABSTRACT FROM AUTHOR]

Embedded computer systems refer to specialized computer systems that integrate operating systems and functional software into computer hardware systems, are application centric, based on computer technology, and have tailorable software and hardware. They have strict requirements for real-time performance, reliability, cost, volume, and power consumption. The performance and security evaluation of embedded systems is an important aspect of the development, development, and integration process of embedded systems, which can provide various quantitative analysis basis for the development and technical transformation of embedded products. The performance and security evaluation of embedded systems is a very complex issue, and evaluators may have different understandings of the problem due to their different starting points and observation perspectives. The embedded computers' performance evaluation is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted geometric Bonferroni mean (GWGBM) operator is built for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number generalized power geometric BM (SVNNGPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNGPGBM operator and power geometric (PG) operator. Finally, an example about embedded computers' performance evaluation and some comparative analysis were given to demonstrate the SVNNGPGBM method. [ABSTRACT FROM AUTHOR]

As society's reliance on software systems escalates over time, so too does the cost of failure of these systems. Meanwhile, the complexity of software systems, as well as of their designs, is also ever‐increasing, influenced by the proliferation of new tools and technologies to address intended societal needs. The traditional response to this complexity in software engineering and software architecture has been to apply rationalistic approaches to software design through methods and tools for capturing design rationale and evaluating various design options against a set of criteria. However, research from other fields demonstrates that intuition may also hold benefits for making complex design decisions. All humans, including software designers, use intuition and rationality in varying combinations. The aim of this article is to provide a comprehensive overview of what is known and unknown from existing research regarding the use and performance consequences of using intuition and rationality in software design decision‐making. To this end, a systematic literature review has been conducted, with an initial sample of 3909 unique publications and a final sample of 26 primary studies. We present an overview of existing research, based on the literature concerning intuition and rationality use in software design decision‐making and propose a research agenda with 14 questions that should encourage researchers to fill identified research gaps. This research agenda emphasizes what should be investigated to be able to develop support for the application of the two cognitive processes in software design decision‐making. [ABSTRACT FROM AUTHOR]

Modeling and simulation have been used to study tsunamis for several decades. We created a review to identify the software and methods used in the last decade of tsunami research. The systematic review was based on the PRISMA methodology. We analyzed 105 articles and identified 27 unique software and 45 unique methods. The reviewed articles can be divided into the following basic categories: exploring historical tsunamis based on tsunami deposits, modeling tsunamis in 3D space, identifying tsunami impacts, exploring relevant variables for tsunamis, creating tsunami impact maps, and comparing simulation results with real data. Based on the outcomes of this review, this study suggests and exemplifies the possibilities of system dynamics as a unifying methodology that can integrate modeling and simulation of most identified phenomena. Hence, it contributes to the development of tsunami modeling as a scientific discipline that can offer new ideas and highlight limitations or a building block for further research in the field of natural disasters. [ABSTRACT FROM AUTHOR]

The development of hard X‐ray nanoprobe techniques has given rise to a number of experimental methods, like nano‐XAS, nano‐XRD, nano‐XRF, ptychography and tomography. Each method has its own unique data processing algorithms. With the increase in data acquisition rate, the large amount of generated data is now a big challenge to these algorithms. In this work, an intuitive, user‐friendly software system is introduced to integrate and manage these algorithms; by taking advantage of the loosely coupled, component‐based design approach of the system, the data processing speed of the imaging algorithm is enhanced through optimization of the parallelism efficiency. This study provides meaningful solutions to tackle complexity challenges faced in synchrotron data processing. [ABSTRACT FROM AUTHOR]

Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing's fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain. [ABSTRACT FROM AUTHOR]

Dependability of software systems is one of the challenging issues for software developers. Main software dependability issues include reliability, security, performability, availability, maintainability, and aging. Software becomes non-dependable due to the overconfidence of developers, lack of knowledge about dependability issues, or ignorance of dependability attributes during software development. Classification and ranking of these non-dependable software modules based on above-mentioned dependability attribute values in early phase are the main aspects of this article. Hence, computation of dependability attribute values becomes a primitive concern here. The values of software dependability attributes depend on various software metrics like: requirement stability, cyclomatic complexity, essential complexity, lines of code, and so on. Neutrosophic inference system (NIS) has been used here to compute the values of dependability attributes accurately, reducing incompleteness, indeterminacy, and impreciseness from metric values by incorporating expert knowledge. An Elman Recurrent Neural Network (ERNN)-based algorithm has been proposed here based on predicted dependability attribute values to classify dependable and non-dependable software modules. Backpropagation algorithm and Genetic Algorithm are used during training of ERNN. Mahalanobis distance (MD) is used to rank software modules based on dependability attributes at early phase of development. This entire process of dependability analysis will help to optimize resource utilization, development cost, and meet the target release time. Different comparison criteria are used to compare the effectiveness of the proposed model with some existing models based on four datasets. Performance analysis demonstrates effectiveness and usefulness for identifying and ranking the non-dependable software modules during early phase of development. [ABSTRACT FROM AUTHOR]

Software systems and product lines often use configurable features to specify a portfolio of product variants from a common core. Typically, their requirements also include constraints on which combinations of features are valid. Especially for larger systems and systems where the specifications are scattered among documents, the analysis of a new product's variability-related requirements is challenging. To address this, we introduce a scalable, tool-supported framework that uses a variability model to automate checks for missing and inconsistent features and constraints. Our approach also extends and scales traditional variability requirements engineering by incorporating combinatorial interaction testing techniques to build valid product variants covering all configurations in the variability model and to automatically discover faulty feature settings in failed builds. Results from evaluation on two configurable systems show that our framework is effective both at early detection of missing, incorrect, and inconsistent variability requirements and at later finding faulty feature configurations. [ABSTRACT FROM AUTHOR]

The scientific literature on vertical gardens presents gaps in the systematization of botanical parameters related to modeling and simulation. To address these gaps, the article aimed to investigate and identify parameters and modeling processes necessary for configuring vertical greening systems in computer simulation software. The systematic literature review method was employed to gather all scientific publications from the past ten years. As a result, the article presents the processes for 3D modeling, the most common software, and information tables containing botanical parameters used for configuring vegetation. Additionally, the work provides an overview of studies conducted on this topic in Architecture in recent years, highlighting other gaps and shortcomings in knowledge for possible future research. [ABSTRACT FROM AUTHOR]

Computational thinking is of great importance in developing students' ability to analyse complex problems in a systematic way, to develop appropriate strategies for solving these problems, and to use technology effectively in applying these solutions. In this context, mathematical modelling activities, which involve solving complex real-life situations through mathematics, stand out as an effective method for developing these skills in students. The purpose of this study is to investigate the effect of mathematical modelling activities on students' computational thinking (CT) self-efficacy. The participants of the study were English preparatory students at a high academic level public school in Istanbul. The modelling activities used in the study were restructured by considering the steps of computational thinking skills. Among these steps, the Python software language was used in the 'algorithmic thinking' step, and the students translated their problem-solving systems into software language through Python. As a data collection tool, in addition to the activities, the CT self-efficacy scale was used as a pre- and post-test. The mathematical modelling activities were applied to the high school preparatory class students, who were the research group, for a period, and the change in the students' CT self-efficacy was evaluated in the process. The results showed that modelling activities led to a significant increase in students' CT self-efficacy. It was found that students felt more competent in sub-dimensions such as problem solving, analytical thinking and algorithmic thinking. These findings suggest that mathematical modelling activities are a powerful tool for developing computational thinking skills. In conclusion, this study suggests that mathematical modelling activities should be more widely incorporated into the secondary school curriculum to develop computational thinking skills and draws attention to the importance of professional support for teachers in this process. [ABSTRACT FROM AUTHOR]

This article by Dr. Hugo Minney discusses the importance of benefits maps in project and program management. Benefits maps visually represent the relationships between activities, outputs, and outcomes, and guide stakeholders through the journey from project inception to the realization of benefits. The article emphasizes the principles of causality, materiality, and clarity when creating benefits maps, and highlights the importance of stakeholder involvement in the process. It also introduces different types of benefits maps, such as bidirectional strategy maps and portfolio benefits maps, and emphasizes the need for continuous refinement and integration with other project management tools. The given text is a list of references and sources related to benefits management and project management, providing additional resources for further research. [Extracted from the article]

In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein's theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI. [ABSTRACT FROM AUTHOR]

This article explores the emerging problem of drug- and alcohol-impaired e-scooter riding. Fifteen government and e-scooter hire firm professionals from four jurisdictions (Australia, Belgium, New Zealand, Norway) described and assessed countermeasures designed to deter drug- and alcohol-impaired riding. Government professionals were from local/municipal councils, state and national departments of transport, and policing organisations. We used Braun and Clarke's (Qual Res Psychol 18(3):328–352, 2020.https://doi.org/10.1080/14780887.2020.1769238) six-step method of thematic analysis and the NVivo qualitative analysis software system to analyse the interviews. All participants said traditional police enforcement is ineffective because laws and regulations are underdeveloped, and police have limited resources for patrols. Thematic analysis grouped countermeasures into four themes: enforcement, education, encouragement, and the road environment. Third-party policing initiatives show promise. For example, local/municipal councils required e-scooter hire firms to ban e-scooters from areas that sell alcohol through GPS-based geofencing. However, geofencing and other regulatory controls do not extend to privately owned e-scooters, a growing sector in the market. Other countermeasures used a combination of education and behavioural change techniques (BCTs) to encourage compliance. For example, innovative nudge methods (a type of BCT) have potential to change dangerous riding behaviours by targeting riding norms. However, participants said some people will continue to ride under the influence of alcohol or drugs either because they are addicted to substances or exhibit high-risk behaviours. Government should therefore adopt laws and regulations that build a more forgiving road environment, based on the safe system approach, to limit injuries when crashes occur. [ABSTRACT FROM AUTHOR]

Auxiliary functions in software systems, often overlooked due to their perceived simplicity, play a crucial role in overall system reliability. This study focuses on the effectiveness of agile practices, specifically the pair programming and the test-first programming practices. Despite the importance of these functions, there exists a dearth of empirical evidence on the impact of agile practices on their development, raising questions about their potential to enhance correctness without affecting time-to-market. This paper aims to bridge this gap by comparing the application of agile practices with traditional approaches in the context of auxiliary function development. We conducted six experiments involving 122 participants (85 novices and 37 professionals) who used both traditional and agile methods to develop six auxiliary functions across three different domains. Our analysis of 244 implementations suggests the potential benefits of agile practices in auxiliary function development. Pair programming showed a tendency towards improved correctness, while test-first programming did not significantly extend the total development time, particularly among professionals. However, these findings should be interpreted cautiously as they do not conclusively establish that agile practices outperform traditional approaches universally. As indicated by our results, the potential benefits of agile practices may vary depending on factors such as the programmer's experience level and the nature of the functions being developed. Further research is needed to fully understand the contexts in which these practices can be most effectively applied and to address the potential limitations of our study. [ABSTRACT FROM AUTHOR]

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method. [ABSTRACT FROM AUTHOR]

Identifying key classes of a software system can help developers understand the system quickly, reduce the time for system maintenance, and prevent security risks caused by defects in key classes. So far, many approaches have been proposed to identify key classes from software systems. However, some approaches select too many key class candidates, making it inconvenient and difficult for developers to start understanding the system from these classes. For the other approaches, although the number of key class candidates is not large, their effectiveness needs to be further improved. To this end, in this paper, we propose a new model, named SAVM, to detect key classes by combining static analysis and a voting mechanism. First, we extract structural information from the source codes of a software system and construct a class coupling network (CCN) using this information. Then, we present the VRWD method that iteratively identifies important nodes in CCN based on a voting mechanism. Specifically, in each iteration, a node votes for its outgoing neighbors and in the meantime receives votes from its incoming neighbors. Afterward, the node that attains the highest voting score is elected as the important node in this turn. Finally, the corresponding classes of the selected important nodes are the key class candidates. The effectiveness of the proposed model and eight other baselines is evaluated in eight open-source Java projects. The experimental results show that although no method performs the best in all projects, according to the average ranking of the Friedman test, our method overall performs better compared to the baselines. In addition, this paper also proves through experiments that our approach can be applied to large-scale software projects. These indicate that our approach is a valuable technique for developers. [ABSTRACT FROM AUTHOR]

Quantum Software, its engineering and its applications are becoming more and more indispensable components of the current knowledge baggage of researchers and industrial professionals. Given the continuous accelerating developments of Quantum Computing, and the vastness of available knowledge, one needs efficient methods to dedicate the limited available personal time to the correct choice of selective information. This Special Issue on "Quantum Computing and Software Engineering" offers a small, but diverse set of papers covering theoretical, practical software system design and a pragmatic survey of Quantum Software Engineering, being a map pointing to references of relevance, aiming to answer the issue of choice of selective information. [ABSTRACT FROM AUTHOR]

Privacy by design is nowadays recognized as essential in bringing data privacy into software systems. However, developers still face many challenges in reconciling privacy and software requirements and implementing privacy protections in software systems. One emerging trend is the adoption of microservices architectures—they bring in some qualities that can benefit privacy by design. The main goal of this study is to adapt privacy by design to the qualities brought by microservices. The main focus is at the architectural level, where the main structural decisions are made. A systematic literature review is adopted to identify a set of privacy models that underscore significant differences in software systems' protection using microservices. From the literature review, a decision framework is developed. The decision framework provides guidance and supports design decisions in implementing data privacy using microservices. The framework helps select and integrate different privacy models. An illustration of using the framework, which considers the design of an electronic voting system, is provided. This study contributes to closing the gap between regulation and implementation through design, where decisions related to data privacy are integrated with decisions on architecting systems using microservices. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for conducting load flow (LF) calculations in radial distribution networks. The method is based on solving LF calculations as a constrained minimization problem using nonlinear programming CONOPT solver, integrated into general algebraic modeling system (GAMS) environment. The performance of the proposed CONOPT solver is evaluated on IEEE 33-bus and IEEE 69-bus test systems, where four cases were considered. In the first case, LF calculations were performed on standard test systems, and the results obtained using the proposed CONOPT solver were compared with three well-known power systems analysis software: EPSA, NEPLAN, and DigSilent. In the second and third cases, CONOPT is used for LF calculation in the presence of distributed generators (DGs) and shunt capacitors (SCs), respectively. In the fourth case, the proposed CONOPT solver is employed for LF calculation in the presence of electric vehicles (EVs). The simulation results show that the CONOPT solver outputs are accurate and fast in their convergence to global solutions in standard test systems in the presence of DGs, SCs, and EVs. [ABSTRACT FROM AUTHOR]

Changes are inevitable in software due to technology advancements, and changes in business requirements. Making changes in the software by insertion, deletion or modification of new code may lead to malfunctioning of the old code. Hence, there is a need for a priori analysis to ensure and capture these types of changes to run the software smoothly. Making changes in the software while it is in use is called dynamic evolution. Due to the lack of formal modeling and verification, this dynamic evolution process of software systems has not become prominent. Hence, we used the bigraphical reactive system (BRS) technique to ensure that changes do not break the software functionality (adversely affect the system). BRS provides a powerful framework for modeling, analyzing, and verifying the dynamic evolution of software systems, resulting in ensuring the reliability and correctness of evolving software system. In this paper, we proposed a formal method technique for modeling and verifying the dynamic evolution process (changing user requirements at run time) using the BRS. We used a bigraph to model software architectures and described the evolution rules for supporting the dynamic changes of the software system. Finally, we have used the BigMC model checker tool to validate this model with its properties and provide associated verification procedures. [ABSTRACT FROM AUTHOR]

Summary: Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity. [ABSTRACT FROM AUTHOR]

In this paper, we design a Spider Monkey-based Elman Spike Neural Network (SM-ESNN) to identify intrusion threats in Software Defined Networks (SDN). Utilizing analysis of multidimensional Internet Protocol (IP) flows to find intrusion and flooding assaults against central controllers. Moreover, information is first gathered from the ISCXIDS2012 dataset and updated to the SDN's secure defensive system. The developed software defense system has two sub-modules: a detection module and a mitigation module. The developed technique's key benefit is improving SDN security by quickly and accurately identifying and stopping assaults. First, the proposed SM-ESNN method is implemented in Python. The assessment measures in this scenario include accuracy, specificity, sensitivity, precision, and false alarm rate (FAR). Furthermore, the suggested SM-ESNN approach obtained improved average performances of 98.24% accuracy, 97.34% specificity, 98.68% sensitivity, and 98.33% precision, which highlights its efficiency in detecting the attacks. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) documentation serves multiple functions, including recording patient health status, enabling interprofessional communication, supporting billing, and providing data to support the quality infrastructure of a Learning Healthcare System. There is no definition and standardized method to assess documentation quality in EHRs. Using a human-centered design (HCD) approach, we define and describe a method to measure documentation quality. Documentation quality was defined as timely, accurate, user-centered, and efficient. Measurement of quality used a virtual simulated standardized patient visit via an EHR vendor platform. By observing and recording documentation efforts, nurse practitioners (NPs) (N = 12) documented the delivery of an Age-Friendly Health System (AFHS) 4Ms (what Matters, Medication, Mentation, and Mobility) clinic visit using a standardized case. Results for timely documentation indicated considerable variability in completion times of documenting the 4Ms. Accuracy varied, as there were many types of episodes of erroneous documentation and extra time in seconds in documenting the 4Ms. The type and frequency of erroneous documentation efforts were related to navigation burden when navigating to different documentation tabs. The evaluated system demonstrated poor usability, with most participants scoring between 60 and 70 on the System Usability Scale (SUS). Efficiency, measured as click burden (the number of clicks used to navigate through a software system), revealed significant variability in the number of clicks required, with the NPs averaging approximately 13 clicks above the minimum requirement. The HCD methodology used in this study to assess the documentation quality proved feasible and provided valuable information on the quality of documentation. By assessing the quality of documentation, the gathered data can be leveraged to enhance documentation, optimize user experience, and elevate the quality of data within a Learning Healthcare System. [ABSTRACT FROM AUTHOR]

Critical systems, in which failure and malfunction may result in severe human, environmental, and financial damages, are essential components in various sectors and particularly in energy domains. Although undesirable, integration error problems in the supervision of critical systems do occur, incurring significant expenses due to an operator's subjective analysis and hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in critical systems' supervision in terms of reliability. A real hybrid power plant (HPP) case is considered for a case study with simulated data. A method framework with an informal approach (C4 diagram) and formal approach (hierarchical colored Petri nets) in a radial spectrum is applied to the HPP supervision design. In using formal methods, a formulation and solution to this problem through structured, scalable, and compact mathematical representations are possible. This malleable model is intended to guarantee the functional correctness and also reliability of the plant supervision system based on system software architecture. The outcomes suggest that the malleable model is appropriate for the energy domain and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied. [ABSTRACT FROM AUTHOR]

The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository. To address this security and privacy concern, we propose a novel approach that efficiently encrypts and decrypts sensitive registry data in real time. Our developed proof-of-concept program intercepts interactions between the registry's application programming interfaces (APIs) and other Windows applications using an advanced hooking technique. This enables the proposed system to be transparent to users without requiring any changes to the operating system or installed software. Our approach also implements the data protection API (DPAPI) developed by Microsoft to securely manage each user's encryption key. Ultimately, our research provides an enhanced security and privacy framework for the Windows registry, effectively fortifying the registry against security and privacy threats while maintaining its accessibility to legitimate users and applications. [ABSTRACT FROM AUTHOR]

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system's historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model's adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks. [ABSTRACT FROM AUTHOR]

This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub. [ABSTRACT FROM AUTHOR]

When creating a software model, it is necessary that it accurately captures the desired behaviour, while at the same time ensuring that any undesired behaviour is excluded. On the one hand, formal verification tools can be used to check the internal consistency of a software system, ensuring that the behaviour of one software component does not contradict another. On the other hand, software testing is essential to check the external validity of the model more comprehensively. Unfortunately, software testing is often overlooked in curricula, resulting in graduates with inadequate software testing skills for industry. Software testing tools such as TesCaV can be used to help teachers teach software testing topics in a non-intrusive and less time-consuming way. Previous research has shown that TesCaV is easy to use and that novice users produce better quality software tests when using TesCaV. However, it has remained unclear whether learners retain the skills they gain from using TesCaV even when the tool is not offered for help. In order to understand the positive effect of TesCaV on learners' software testing skills, this study conducted an experiment with 45 participants. The experiment used a pretest-treatment-posttest design. The results show that participants feel equally confident about the completeness of their test coverage, even though they identify more test cases. It is concluded that for course design, a capsule such as TesCaV can help students to understand the full complexity of software testing and help them to be more systematic in their approach. [ABSTRACT FROM AUTHOR]

This paper introduces a novel digital triangular–trapezoidal double-channel shaping algorithm to enhance the counting rate of resistive anode detectors. The algorithm is based on the trapezoidal shaping algorithm and improves it. At the extreme counting rate, the trapezoidal shaping algorithm cannot alleviate the pulse pileup, so the counting rate cannot meet the requirements of a high performance detector. The triangular–trapezoidal double-channel shaping algorithm is introduced in the resistance anode detector, which can replace the trapezoidal shaping filtering algorithm to process the output signal of the resistance anode detector and obtain the single photon position information. This improvement improves the counting rate of the resistor anode detector and reduces the resolution degradation caused by pulse pileup. The algorithm is simulated by System Generator software and implemented on FPGA (field programmable gate array). The triangular–trapezoidal double-channel shaping algorithm presented in this paper plays an important role in reducing electronic noise and pulse pileup. The algorithm is subjected to simulation testing, and it can recognize signals with a minimum pulse interval of 1 µs and counting rate up to 1000 kcps. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

The goal of the work described here was to develop the first neuronavigation-guided transcranial histotripsy (NaviTH) system and associated workflow for transcranial ablation. The NaviTH system consists of a 360-element, 700 kHz transmitter–receiver-capable transcranial histotripsy array, a clinical neuronavigation system and associated equipment for patient-to-array co-registration and therapy planning and targeting software systems. A workflow for NaviTH treatments, including pre-treatment aberration correction, was developed. Targeting errors stemming from target registration errors (TREs) during the patient-to-array co-registration process, as well as focal shifts caused by skull-induced aberrations, were investigated and characterized. The NaviTH system was used in treatments of two <96 h post-mortem human cadavers and in experiments in two excised human skullcaps. The NaviTH was successfully used to create ablations in the cadaver brains as confirmed in post-treatment magnetic resonance imaging A total of three ablations were created in the cadaver brains, and targeting errors of 9, 3.4 and 4.4 mm were observed in corpus callosum, septum and thalamus targets, respectively. Errors were found to be caused primarily by TREs resulting from transducer tracking instrument design flaws and imperfections in the treatment workflow. Transducer tracking instrument design and workflow improvements reduced TREs to <2 mm, and skull-induced focal shifts, following pre-treatment aberration correction, were 0.3 mm. Total targeting errors of the NaviTH system following the noted improvements were 2.5 mm. The feasibility of using the first NaviTH system in a human cadaver model has been determined. Although accuracy still needs to be improved, the proposed system has the potential to allow for transcranial histotripsy therapies without requiring active magnetic resonance treatment guidance. [ABSTRACT FROM AUTHOR]