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Design patterns are an effective technique for software development. However, lacking suitable documents often reduces traceability, and advantages remain unclear in source lines of code (SLOC). Design patterns are proven, reliable solutions to several frequent problems in object‐oriented software design. Detecting design patterns in a software system contributes to maintainability and reengineering and facilitates understanding codes. Furthermore, different design pattern implementations impede the detection of code pattern instances. A design pattern is a set of roles played by the code classes. Roles are the main details and determinants in patterns. A pattern can be detected in its defined role as it is an instance of a role. It is also of great importance in system documentation. The proposed method exploits blockchain‐based approaches for pattern mining and isolating design patterns in order to identify intercode relationships. The results demonstrate that the blockchain is effective and efficient in extracting the connections of features from source codes and adapting to design patterns with precision 97.2%. [ABSTRACT FROM AUTHOR]

Accidental vessel collisions pose a threat to the safety of train operations on navigable bridges. The CRH2 high-speed train, operating on a four-span continuous girder bridge, is selected as a research prototype to study the effects of vessel impact loads on the responses of the train-track-bridge (TTB) system and train running safety. The TTB model subjected to vessel collision is developed using the finite element software ANSYS and the multi-body system software SIMPACK. The influences of load intensity and train speed on the critical indicators of train running safety are discussed. Safety thresholds based on train running speed are proposed through comprehensive parametric analysis. The results indicate that the impact load has a significant effect on the dynamic responses of both bridge and track structures. The interaction between piles and soil (PSI) has a significant impact on the dynamic responses of the TTB system. The track structure undergoes significant overall deformation under the vessel impact load. The train running safety indices ascend with the increase in impact load intensity and train speed. As the impact load intensity increases from 0 to 24.25 MN, the speed limit for ensuring train running safety decreases from 350 km/h to 250 km/h. [ABSTRACT FROM AUTHOR]

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. We also consider how such a prover can be used to solve a reasoning task without much mental labor. The development is extended with a formalized proof system for writing machine-checked sequent calculus proofs. We consider how this can be used to teach computer science students about logic, automated reasoning and proof assistants. [ABSTRACT FROM AUTHOR]

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality. [ABSTRACT FROM AUTHOR]

Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems.EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher).Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research. [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]

For highly reliable software systems, it is expensive, time consuming, or even infeasible to perform reliability testing via a conventional software reliability demonstration testing (SRDT) plan. Moreover, in the traditional SRDT approach, the various characteristics of the software system or test sets are not considered when making the testing schemes. Some studies have focused on the effect of software testability on SRDT, but only limited situations were discussed, and many theoretical and practical problems have been left unresolved. In this paper, an extended study on the quantitative relation between test effectiveness (TE) and test effort for SRDT is proposed. Theoretical derivation is put forward by performing statistical analysis for the test suite according to TE. The combinations of all the cases of zero-failure and revealed nonzero failure, as well as discrete-type software and continuous-type software, are studied with the corresponding failure probability models constructed. That is, zero-failure and nonzero failure, as well as discrete-type software and continuous-type software, respectively, constitute the symmetry and asymmetry of SRDT. Finally, we illustrated all the models and performed applications on the Siemens program suite. The experimental results show that within the same limitation of requirements and confidence levels, this approach can effectively reduce the number of test cases and the test duration, i.e., accelerate the test process and improve the efficiency of the SRDT. [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]

During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement. [ABSTRACT FROM AUTHOR]

Due to VDES's higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources. [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]

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]

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]

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]

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]

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]

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]

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]

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]

The continuous evolution of malware poses a significant challenge in cybersecurity, adapting to technological advancements despite implemented security measures. This paper introduces an innovative approach to enhance the detection of obfuscated malware through the integration of machine learning (ML). Utilizing a real-world dataset of prevalent malware types such as spyware, ransomware, and trojan horses, our study addresses the evolving challenges of cybersecurity. In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. Experimental results demonstrate the effectiveness of RF, J-48, and XGBoost in achieving high accuracy rates across different classification tasks. NB also shows competitive performance but faces challenges in handling imbalanced datasets and multi-class classification. Our findings highlight the importance of employing advanced ML techniques for enhancing obfuscated malware detection capabilities and provide valuable insights for cybersecurity practitioners and researchers. Future research directions include finetuning model hyperparameters, exploring ensemble learning approaches, and expanding evaluation to diverse datasets and real-world scenarios. [ABSTRACT FROM AUTHOR]

Software systems are often maintained by a group of experienced software developers in order to ensure that faults that may bring the system down are less likely. Large turnover in organizations such as CERN makes it important to think of ways of onboarding newcomers on a technical project rapidly. This paper focuses on optimizing the way that people get up-to-speed on the business logic and technologies used on the project by using a knowledge-imbued large language model that is enhanced using domain-specific knowledge from the group or team's internal documentation. The novelty of this approach is the gathering of all of these different open-source methods for developing a chatbot and using it in an industrial use-case. [ABSTRACT FROM AUTHOR]

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software's effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. [ABSTRACT FROM AUTHOR]

[Background and Objective] In order to overcome the difficulty about laying electric wires in the field and realize unmanned filed irrigation, improving the intellectualization and precision of irrigation in rice planting area in Anhui Province and exploring the feasibility of intelligent control of irrigation sluice by using new generation electronic information technology would be carried out in the research. [Method] An intelligent irrigation control system based on AT89S52 single chip computer was designed in this paper, which included integrated lifting sluice, irrigation hardware subsystem and irrigation software subsystem. The gate of the sluice was adopted integrated design, which was integrated central processing unit module, sensor sensing module and solar power supply module based on AT89S52 MCU. Keil C developed the control software for the terminal of the gate, and the upper computer control software of the system was developed by C++. [Result] The application test showed that the automatic control response time of the motor was less than 1 s, the gate of the sluice opening control error was less than 1.5 mm, the water level control error was less than 8 mm, the maximum packet loss rate of data transmission was 0.11%, the calculation error of free discharge flow was less than 1%, and that of submerged outflow flow was less than 1.2%. [Conclusion] This system run safely and stably, and could meet the requirement of intelligent and precise water use in the complex environment of field irrigation of rice planting area. [ABSTRACT FROM AUTHOR]

Background: Approaches to documenting the software patterns of a system can support intentionally and manually documenting them or automatically extracting them from the source code. Some of the approaches that we review do not maintain proximity between code and documentation. Others do not update the documentation after the code is changed. All of them present a low level of liveness. Approach: This work proposes an approach to improve the understandability of a software system by documenting the design patterns it uses. We regard the creation and the documentation of software as part of the same process and attempt to streamline the two activities. We achieve this by increasing the feedback about the pattern instances present in the code, during development—i.e., by increasing liveness. Moreover, our approach maintains proximity between code and documentation and allows us to visualize the pattern instances under the same environment. We developed a prototype—DesignPatternDoc—for IntelliJ IDEA that continuously identifies pattern instances in the code, suggests them to the developer, generates the respective pattern-instance documentation, and enables live editing and visualization of that documentation. Results: To evaluate this approach, we conducted a controlled experiment with 21 novice developers. We asked participants to complete three tasks that involved understanding and evolving small software systems—up to six classes and 100 lines of code—and recorded the duration and the number of context switches. The results show that our approach helps developers spend less time understanding and documenting a software system when compared to using tools with a lower degree of liveness. Additionally, embedding documentation in the IDE and maintaining it close to the source code reduces context switching significantly. [ABSTRACT FROM AUTHOR]

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra. [ABSTRACT FROM AUTHOR]

This article summarizes the authors' experiences in the development and application of the General Dynamical Model Approach related to adaptive linearizing control of biotechnological processes. Special attention has been given to some original, innovative solutions in model-based process control theory: new formalization of biotechnological process kinetics, derivation and tuning of the general software sensor of the full kinetics of biotechnological processes, and a general algorithm for fully adaptive linearizing control with software sensors. These theoretical solutions are the basis of three control strategies—fully adaptive control of the main substrate, partially adaptive control of intermediate metabolite, and recognition and stabilization of the desired physiological state based on the proposed theoretical solutions. Each strategy is illustrated in different case studies. The advantages and limitations of each of them are identified and discussed. The derived algorithms for monitoring and controlling the considered biotechnological processes are realized and included in a software platform named Interactive System for Education in Modelling and Control of Bioprocesses (InSEMCoBio). The InSEMCoBio modules and their main functions are discussed. The effectiveness of the proposed control strategies (achieving maximum productivity) has been proven through a series of simulation investigations of the considered case studies. [ABSTRACT FROM AUTHOR]

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design. [ABSTRACT FROM AUTHOR]

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

The field of self-adaptive software is becoming increasingly crucial because software has to adapt its behavior at runtime to keep up with dynamic and constantly evolving environments. The ability of software to modify and adjust itself is known as adaptability, which has been acknowledged as an important quality attribute. As software architecture development represents the initial stage of the design process, architectural design has emerged as a critical development activity. The degree to which the architecture can adapt to changes will be the key factor in determining the adaptability of the software ultimately released to users. Existing metrics for software architectural adaptability typically view adaptability as a positive attribute, meaning that any possibility of change in architectural elements is perceived as an improvement in adaptability. However, the application of adaptability that leads to increased costs or complexity can only be seen as a negative attribute. In other words, the side effects brought about solely for the purpose of achieving adaptability are greatly overlooked; that is, the impacts caused by adaptability exhibit both symmetry and asymmetry for software architectural adaptability. On the other hand, the existing measures of adaptability only define it from the perspective of whether a module will change, without considering changes beyond the topological structure under multiple strategies. In this paper, we propose a comprehensive assessment approach to measure software architecture adaptability. Multiple factors are considered, including the number and types of strategies employed, the cost of completing the adaptation, the extent of changes made to the architectural elements, and the overall impact of the adaptation on the architecture. An extended UML (e-UML) component diagram is presented to model software architectural changes under various types of strategies. Finally, an experiment on the znn.com software is conducted to validate the practical feasibility of our proposed method. Our approach can assist software architects in making informed decisions regarding software architecture design options to improve the adaptability and longevity of their software systems. [ABSTRACT FROM AUTHOR]

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made. [ABSTRACT FROM AUTHOR]

The cryptogenic nuisance alga Chondria tumulosa was first observed in 2016 at Manawai (Pearl and Hermes Atoll) in the Papahānaumokuākea Marine National Monument. It has since spread across the atoll, growing in thick mats and smothering benthic habitat. In September 2021 the species was observed at Kuaihelani (Midway Atoll), ~130 km to the northwest. Due to its growth habit and recent spread, considerable concern has been raised and management of the species may be warranted. We used publicly available oceanographic data and the Connectivity Modeling System software to assess how the potential for successful dispersal of C. tumulosa is affected by particle properties and oceanographic conditions. We found the likelihood of successful transit to be linked to particle density, oceanographic conditions at the time of release, and release location. Further modeling explicitly targeted the capacity of both reproductive tetraspores as well as drifting fragments to disperse. Model results indicated tetraspores of C. tumulosa are unlikely to survive the transit from Manawai to Kuaihelani, as none arrived at Kuaihelani above the depth limit of the species and those arriving below successfully settled at a rate of only 0.02%. In contrast, fragments modeled as rafting on marine flotsam such as macroalgae and marine debris settled at a rate of 3.85%. Rafting fragments also settled ~600 km further to the southeast (towards the Main Hawaiian Islands) than tetraspores. This study identified oceanographic conditions and particle properties likely to aid dispersal of C. tumulosa to Kuaihelani and suggests that fragments rafting on marine flotsam may accelerate its spread. [ABSTRACT FROM AUTHOR]

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The author has designed an automated monitoring system for settlement and deformation of high-speed railway subgrade. Firstly, an automated monitoring system is designed based on sensors, data collection and transmission, client tracking and querying, monitoring result processing, automated warning, manual monitoring data analysis, monitoring data analysis and evaluation. Secondly, the system software and hardware are designed, the author calculated and analyzed engineering examples from the perspective of practical applications using artificial neural network methods, obtained corresponding deformation analysis models, and predicted deformation. Finally, the practical application of the system was analyzed for its effectiveness. The experimental results indicate that, the BP artificial neural network method is used to model and predict the Deformation monitoring data. On the premise of 20 learning samples and 4 prediction samples, the Root-mean-square deviation of the prediction is 0.32mm, which shows that the deformation prediction using BP model is feasible and effective in a certain precision range. It has been proven that the application of artificial neural network methods in monitoring and prediction of practical engineering has certain practical significance. [ABSTRACT FROM AUTHOR]

The DevOps pipeline for infrastructure is a critical component in modern software development and operations practices. It involves automating the provisioning, configuration, and management of infrastructure resources, enabling organizations to achieve agility, scalability, and reliability. This paper presents a plagiarism-free analysis of the DevOps pipeline for infrastructure, conducted through comprehensive research, evaluation of industry best practices, and examination of case studies. The DevOps methodology would collapse without the use of a DevOps pipeline. The phrase is often used to discussions of the methods, procedures, and automation frameworks that go into the creation of software objects. Jenkins, an open-source Java program, is the most well-known DevOps pipeline and is often credited as the catalyst for the whole DevOps movement. Today, we have access to a plethora of DevOps pipeline technologies, such as Travis CI, GitHub Actions, and Argo. To keep up with the need for new and improved software systems, today's development organizations must overcome a number of obstacles. The research highlights key findings, including the importance of automation, infrastructure as code, continuous integration and delivery, security, and monitoring/logging capabilities. These practices have been shown to enhance efficiency, reduce errors, and accelerate deployment cycles. By evaluating tools and technologies, gathering user feedback, and analyzing performance metrics, organizations can identify gaps and develop a roadmap for pipeline improvement. To maintain academic integrity, this analysis adheres to proper citation and referencing practices. Paraphrasing and summarizing research findings and adding personal analysis and interpretations ensure the originality and authenticity of the analysis. Plagiarism detection tools are used to confirm the absence of unintentional similarities with existing content. [ABSTRACT FROM AUTHOR]

The Brunswick modular framework for modeling unsaturated soil hydraulic properties (SHP) over the full moisture range was implemented in the Hydrus suite. Users can now additionally choose between four different variants of the Brunswick model: (i) van Genuchten–Mualem (VGM), (ii) Brooks–Corey, (iii) Kosugi, and (iv) modified van Genuchten. For demonstration purposes, simulation results for two different setups, (i) bare soil evaporation and (ii) root water uptake, are presented, along with a comparison of the original VGM model and its Brunswick variant. Results show that the original VGM model underestimates the simulated cumulative evaporation and cumulative transpiration due to the inconsistent representation of the SHP in the dry moisture range. We also implemented a two‐step hydro‐PTF (pedotransfer function) into the Hydrus suite that converts the parameters of the original VGM model (from Rosetta) to the corresponding Brunswick variant. In that way, physically comprehensive simulations are ensured if no data on SHP are directly available, but information on physical soil properties (e.g., texture and bulk density) exists. Core Ideas: A modular framework was implemented in Hydrus to describe soil hydraulic properties.The new model is more robust in describing soil hydraulic properties in the dry moisture range.Hydrus simulations with the new model predict high bare soil evaporation and transpiration fluxes. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated. [ABSTRACT FROM AUTHOR]

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort. [ABSTRACT FROM AUTHOR]

Background: Minimizing muscle strain and reducing the risk of musculoskeletal disorders associated with intraoral scanner (IOS) usage require ergonomic awareness, device selection, and workplace adjustments in dental practice. This preliminary clinical study aimed to simulate intraoral scanning tasks using wired and wireless IOSs and assess muscle activation and fatigue for both types. Materials and methods: Fourteen participants performed intraoral scanning tasks using wired and wireless IOSs (i700; MEDIT), with weights of 280 g and 328 g, respectively. The same computer system and software conditions were maintained for both groups (N = 14 per IOS group). Electrodes were placed on arm, neck, and shoulder muscles, and maximal voluntary contraction (MVC) was measured. Surface electromyography (EMG) was performed during the simulation, and EMG values were normalized using MVC. The root mean square EMG (%MVC) and muscle fatigue (%) values were calculated. Statistical comparisons were performed using the Mann–Whitney U and Friedman tests, with the Bonferroni adjustment for multiple comparisons (α = 0.05). Results: Arm (flexor digitorum superficialis) and neck muscles (left sternocleidomastoid and left splenius capitis) showed significantly higher EMG values with wireless IOS (P < 0.05). The neck (left sternocleidomastoid and right levator scapulae) and shoulder muscles (right trapezius descendens) demonstrated significantly higher muscle fatigue with wireless IOS (P < 0.05). Conclusions: The consecutive use of heavier wireless IOS may increase the risk of muscle activation and fatigue in certain muscles, which may have clinical implications for dentists in terms of ergonomics and musculoskeletal health. [ABSTRACT FROM AUTHOR]

Highly reliable software systems rarely fail during tests because they are usually designed with fault-tolerant mechanisms and tested comprehensively. It is usually difficult to obtain sufficient failure data to carry out reliability measurements by using traditional software reliability models. These models are typically based on probabilistic statistics, and the measurement accuracy cannot be guaranteed with insufficient failure data. We propose a nonparametric bootstrap (NBP) resampling method and six parametric bootstrap (PB) resampling methods to construct software reliability models for small sample conditions based on commonly used models, i.e., the Jelinski–Moranda (J–M), Goel–Okumoto (G–O), Musa–Okumoto (M–O), Schneidewind, Duane and Littlewood-Verrall models. The bootstrap is a statistical procedure that resamples a single dataset to create many simulated samples. Our experimental results on fourteen failure datasets collected from industry and academia show that the proposed models improve by 10.2–18.0% failure time prediction accuracy, 24.7–30.7% curve fitting accuracy, and 7.7–42.9% reliability measurement accuracy compared with the original models. Furthermore, our approaches achieve 58.3–91.1% better failure time prediction accuracy in the case of small sample conditions compared to state-of-the-art machine learning and neural network-based methods. Overall, our approaches can perform more accurate reliability measurements than the original models even in scenarios with limited failure data. [ABSTRACT FROM AUTHOR]

This paper examines the paradoxical transparency involved in training machine-learning models. Existing literature typically critiques the opacity of machine-learning models such as neural networks or collaborative filtering, a type of critique that parallels the black-box critique in technology studies. Accordingly, people in power may leverage the models' opacity to justify a biased result without subjecting the technical operations to public scrutiny, in what Dan McQuillan metaphorically depicts as an "algorithmic state of exception". This paper attempts to differentiate the black-box abstraction that wraps around complex computational systems from the opacity of machine-learning models. It contends that the degree of asymmetry in knowledge is greater in the former than the latter. In the case of software systems, software codes are difficult to understand as only software experts with sufficient domain knowledge are equipped to formulate a sound critique. In contrast, the meanings of trained parameters in a machine-learning model are obscure even to the data scientists who configure and train the model. Hence, the asymmetry of knowledge lies only in how data examples are collected, the choice and configuration of machine-learning models, and the specification of features in model design. Under the trend of algorithmic decision-making proliferating with machine-learning heuristics, the paper contends that the more symmetric distribution of knowledge in machine learning could lead to a more transparent production process if proper policies are in place. [ABSTRACT FROM AUTHOR]

In this article, the development of a software system—a simulator for testing software systems using a binary protocol—is proposed. The purpose of the development is to achieve full testing of a wide class of software systems. They are directly related to the management of hardware systems, with which communication is based on a binary protocol. The developed simulator allows different types of messages to be simulated during real communication. In this way, it is possible to check how a given program system would react to correct and incorrect messages in the communication stage. This article presents a GUI system developed in the standard C++ programming language and the Qt platform, which makes it platform-independent. The system provides the ability to select preset commands via an external file or enter/correct commands in real-time. The commands may be different depending on the integration scope of the binary protocol. The connection between the developed simulator and the tested system is through RS 232 interface. Configuring settings and testing binary protocols through the developed application becomes fast and intuitive. [ABSTRACT FROM AUTHOR]

The liquid dosing system consists of machines and a computer (PC). The machines and the computer are connected to each other and communicate via the RS485 interface. The software installed on a computer (PC) consists of two separate modules (applications)—"server" and "operator". The server runs continuously and communicates to and from the machines. Through the operator module, the nomenclature of customers, liquids, and cards can be created and edited. The operator module makes it possible to perform various operations such as loading credit on a card, blocking and unblocking cards, editing customer or card data, and others. Various reports can be made, such as a report on the consumption of a given customer, a report on the use of the customer card, a report on the liquid consumed, etc. The goal of the system is to perform automated dosing of liquid at the customer's request, without the involvement of another person (operator). The choice of liquid, the amount, and the payment of the choice made is fully automatic. Payment is only by card, as in case of an attempt to make a dosage and insufficient availability for payment, the dosage is not carried out. The customer is informed of a small credit on the card. The connection between the machine and the operator display is based on a query from the display. The operator's job involves replacing empty bottles in the machine and monitoring and following up on system irregularities. [ABSTRACT FROM AUTHOR]