Diverse Software Research Articles

Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI

Artificial Intelligence (AI) is a pivotal domain within computer science, profoundly influencing the software development lifecycle, particularly during the implementation phase. Here, developers grapple with the task of translating software requirements and designs into executable code. Automated Code Generation (ACG) leveraging AI emerges as a promising solution in this context. The automation of code generation processes is gaining traction as a means to tackle diverse software development challenges while boosting productivity. This paper presents a thorough review and discourse on both traditional and AI-driven techniques employed in ACG, highlighting their respective challenges and constraints. Through an examination of pertinent literature, we identify various AI methodologies and algorithms utilized in ACG, extracting evaluation metrics such as Accuracy, Efficiency, Scalability, Correctness, Generalization, among others. These metrics serve as the basis for a comparative analysis of AI-driven ACG methods, delving into their applications, strengths, weaknesses, performance metrics, and future prospects.

Accessibility Technologies in Esports and Their Impact on Quality of Life in People with Physical Disabilities

This study addresses the multifaceted challenges faced by individuals with physical disabilities, encompassing health, economic, and social aspects. The sustainable development of societies hinges on the collective contributions of both disabled and healthy individuals. Esports has recently emerged as a promising avenue for the integration of individuals with physical disabilities into societal life, primarily due to its capacity to transcend physical and demographic constraints. This research therefore aimed to explore the impact of accessibility technologies employed in esports on the quality of life for individuals living with physical disabilities. For this purpose, a literature review was conducted to examine the effect of accessibility technologies currently used in esports on the quality of life of physically disabled individuals. A comprehensive review of the literature suggests that esports, coupled with diverse software and hardware technologies designed to enhance the accessibility of individuals with physical disabilities to esports, yields positive outcomes, which encompass improved socialization, enhanced career opportunities within esports, heightened cognitive and intellectual skills, ultimately leading to an elevated quality of life. As a result, it can be said that these technologies, in addition to widening accessibility to esports, hold the potential to facilitate the organization of tailored esports tournaments and events for disabled individuals, thereby encouraging greater participation in the esports realm.

Software System for Automatic Grading of Paper Tests

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.

Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks.

Previous studies have established the utility of facial expressions as an objective assessment approach for determining the hedonics (overall pleasure) of food and beverages. This study endeavors to validate the conclusions drawn from preceding research, illustrating that facial expressions prompted by tastants possess the capacity to forecast the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants, aged 18-55 years, were captured using a digital camera during their consumption of diverse concentrations of solutions representative of five basic tastes. Employing the widely employed facial expression analysis application FaceReader, the facial expressions were meticulously assessed, identifying seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) characterized by scores ranging from 0 to 1-a numerical manifestation of emotional intensity. Simultaneously, participants rated the hedonics of each solution, utilizing a scale spanning from -5 (extremely unpleasant) to +5 (extremely pleasant). Employing a multiple linear regression analysis, a predictive model for perceived hedonic ratings was devised. The model's efficacy was scrutinized by assessing emotion scores from 11 additional taste solutions, sampled from 20 other participants. The anticipated hedonic ratings demonstrated robust alignment and agreement with the observed ratings, underpinning the validity of earlier findings even when incorporating diverse software and taste stimuli across a varied participant base. We discuss some limitations and practical implications of our technique in predicting food and beverage hedonics using facial expressions.

Identification of biomarkers and construction of a microRNA‑mRNA regulatory network for hepatocellular carcinoma using integrated bioinformatic analysis

Objective: By applying bioinformatic techniques, this study aims to explore potential biomarkers associated with hepatoma carcinoma (HCC) through comprehensive analyses. Methods: We downloaded two RNA microarray datasets related to HCC by accessing the Gene Expression Omnibus (GEO) database and employing specific keywords. Subsequently, all data were retrieved and processed to analyze the differential miRNAs and mRNAs within these datasets. Moreover, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted, followed by the construction of protein-protein interaction (PPI) network. Utilizing diverse software and online platforms, we created volcano maps, GO analysis diagrams, KEGG analysis diagrams, protein interaction network diagrams, and miRNA-mRNA interaction analysis diagrams. Results: 13 differential miRNAs and 256 differential mRNAs were extracted from the two datasets. The GO annotation encompasses over 1,000 entries, comprising 345 biological process (BP) entries, over 490 cellular component (CC) entries, and 165 molecular function (MF) entries. Additionally, 116 signaling pathways were identified from the general set pathway of the KEGG. Conclusions: This study utilized bioinformatics research methods to construct a miRNA-mRNA interaction network by further collating, refining and analyzing the data obtained from miRNA chips and mRNA chips. Through the mining of potential biomarkers of HCC, new insights have been proposed for future experimental research.

Research on the POPi Digital Model Framework for BIM Implementation in High-Rise Megaprojects

For more than a decade, researchers and practitioners have been working to advance the implementation of virtual design and construction (VDC) and building information modeling (BIM) in the construction industry. Based on the product-organization-process (POP) research model of VDC/BIM, this paper presents the product-organization-process-infrastructure (POPi) digital framework by adding the core element: the software and hardware infrastructure. This paper discusses the element models and corresponding relationship of the POPi framework in terms of practical characteristics: the diversity of BIM applications and software in high-rise megaprojects and cross-organizational task interdependence. In addition, by combining the practices of the Suzhou Zhongnan Center, this study analyzes the four core element models of the POPi framework and discusses the typical applications and benefits to projects in design and the early stage of construction based on the element models. The research results provide a theoretical framework for the BIM application and related software development of similar projects.

Implementing Centralized Error Handling for Software Systems through the Integration of Machine Learning Techniques

Based on the increase of data volumes in the current world, modern software solutions' complexities and transaction volume make it imperative to establish more efficient and robust error-handling approaches. Traditional strategies have struggled to cope with the dynamics of voluminous transactions known to be decentralized and ad-hoc. This has led to operational disruptions and diverse software efficacy disruptions. The central prism of this paper points to how to leverage a proposed central error-handling system (CEHS). The proposition encapsulates how machine learning techniques can be leveraged to address these challenges. Discussing the limitations of current error-handling methods and highlighting benefits stemming from a centrist approach are embedded in this discourse. Consequently, this thread explores integrating ML algorithms as a prerequisite for identifying anomalies, triggering remedial actions, and predicting errors that could stem from the scenario. The remit of this discourse is to present a framework for CEHS implementation and discuss the potential impact regarding the reliability and performance of high transactional volumes through the prism of novel approaches in ML like data science skills and software engineering skills.

Comparison of Quantitative X-ray Diffraction Mineral Analysis Methods

X-ray diffraction (XRD) analysis, as one of the most powerful methods, has been widely used to identify and quantify minerals in earth science. How to improve the precision of mineral quantitative analysis is still a hot topic. To date, several quantitative methods have been proposed for different purposes and accompanied by diverse software. In this study, three quantitative mineral analysis methods, including the reference intensity ratio (RIR), Rietveld, and full pattern summation (FPS) methods, are compared and evaluated to systematically investigate their accuracy and applicability. The results show that the analytical accuracy of these methods is basically consistent for mixtures free from clay minerals. However, there are significant differences in accuracy for clay-mineral-containing samples. In comparison, it seems that the FPS method has wide applicability, which is more appropriate for sediments. The Rietveld method has been shown to be capable of quantifying complicated non-clay samples with a high analytical accuracy; nevertheless, most conventional Rietveld software fails to accurately quantify phases with a disordered or unknown structure. The RIR method represents a handy approach but with lower analytical accuracy. Overall, the present results are expected to provide a potentially important reference for the quantitative analysis of minerals in sediments.

Towards an Integrated Methodology and Toolchain for Machine Learning-Based Intrusion Detection in Urban IoT Networks and Platforms

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often generate or collect sensitive, real-time data used in critical application scenarios (e.g., health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victim of malicious actors. In this paper, we presented a brief overview of existing attacks and defense strategies and used this as motivation for proposing an integrated methodology for developing protection mechanisms for smart city IoT networks. The goal of this work was to lay out a theoretical plan and a corresponding pipeline of steps, i.e., a development and implementation process, for the design and application of cybersecurity solutions for urban IoT networks. The end goal of following the proposed process is the deployment and continuous improvement of appropriate IoT security measures in real-world urban IoT infrastructures. The application of the methodology was exemplified on an OMNET++-simulated scenario, which was developed in collaboration with industrial partners and a municipality.

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed.

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

A novel encryption algorithm using multiple semifield S-boxes based on permutation of symmetric group

There is a need of secure and robust encryption algorithm that can be implemented on each and diverse software and hardware platforms. In this paper, we investigate semifield substitution boxes using permutation of symmetric group on a set of size 8 S_8 and establish an effective procedure for generating S_8 semifield substitution boxes having same algebraic properties. Further, the strength analysis of the generated substitution boxes is carried out using the well-known standards, namely bijectivity, non-linearity, strict avalanche criterion, bit independence criterion, XOR table and differential invariant. As application, an encryption algorithm is proposed that can be employed to strengthen any kind of secure communication. The presented algorithm is mainly based on the Shannon idea of substitution–permutation network where the process of substitution is performed by the proposed S_8 semifield substitution boxes and permutation operation is performed by the binary cyclic shift of substitution box transformed data. In addition, the proposed encryption algorithm utilizes two different chaotic maps. To ensure the appropriate utilization of these chaotic maps, we carry out in-depth analyses of their behavior in the context of secure communication and apply the pseudo-random sequences of chaotic maps in the proposed image encryption algorithm accordingly.

Customized Zirconia Crown in Pediatric Dentistry From Concept to Reality

Catering a tooth-colored restoration in a single sitting is the fundamental objective of chairside digital dentistry with computer-aided design/computer-assisted manufacturing (CAD/CAM) technology, which became a legitimate reality with the initiation of ceramic reconstruction (CEREC) workflow. CAD/CAM dentistry has evolved through an amalgamation of diverse software and hardware upgrades since its launch to a viable chairside technology that allows pediatric dentists to treat patients in a single visit. Nowadays, CAD/CAM of dental restorations has become an ingrained fabrication process, especially for zirconium restorations. In this report, we have presented three cases to exemplify the clinical use of chairside digital dentistry (i.e., CEREC workflow) for the fabrication of a customized zirconium restoration in a single sitting to restore form, function, and occlusion for grossly decayed and decalcified primary molars, as well as esthetics for primary anterior teeth with utmost comfort of child patients with single-sitting treatment modality.

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code, which often requires specific treatment for each platform. The problem becomes critical on embedded devices, where computational and memory resources are strictly constrained. Compilers play an essential role in deploying source code on a target device through the backend. In this work, a novel backend for the Open Neural Network Compiler (ONNC) is proposed, which exploits machine learning to optimize code for the ARM Cortex-M device. The backend requires minimal changes to Open Neural Network Exchange (ONNX) models. Several novel optimization techniques are also incorporated in the backend, such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations. The performance of the proposed framework is evaluated for two applications: handwritten digit recognition on the Modified National Institute of Standards and Technology (MNIST) dataset and model, and image classification on the Canadian Institute For Advanced Research and 10 (CIFAR-10) dataset with the AlexNet-Light model. The system achieves 98.90% and 90.55% accuracy for handwritten digit recognition and image classification, respectively. Furthermore, the proposed architecture is significantly more lightweight than other state-of-the-art models in terms of both computation time and generated source code complexity. From the system perspective, this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

DEVELOPMENT OF INTELLECTUAL SUPPORT’S ALGORITHM AT MANAGE DECISIONS MAKING IN THE FEEDING PROCESS BASED ON A MACHINE VISION SYSTEM’S ON A FARM

Analysis of information obtained from open sources has shown that feed’s abnormal overspending is up to 10% and occurs in 5% of cases. It also means that the farm loses the same share of milk yield, and with it profit. This problem solution can be achieved in various ways due diversity of both hardware (vision system) and software (methods and algorithms). It is possible cows’ feed on dairy farms malnutrition reducing with the help of software and hardware complex (PAC) being developed. This PAC will determine the remaining feed volume on the feed table’s distribution by the 3D vision system of obtaining data use analyzing. Based on this analysis, the PAC will draw conclusions and make recommendations to the farm workers, thereby feedback for the farm feeding system providing. To do this, it is necessary a vision system to choose, an algorithm for obtaining a point cloud to implement an algorithm for finding reference points and gluing images into one, so as an device algorithm for orientation in space to select and implement, an algorithm for calculating feed volume and hardware to develop and laboratorial and operational tests to conduct, as well as a database for development of recommendations to create. Two vision systems are considered for use - a stereo system and a ToF camera, and an algorithm finding for reference points based on 3D data is described.

Software reliability model for open-source software that considers the number of finite faults and dependent faults.

Software has become a vital factor in the fourth industrial revolution. Owing to the increase in demand for software products in various fields (big data, artificial intelligence, the Internet of Things, etc.), the software industry has expanded more than ever before. Therefore, software reliability has become very important, and efforts are being made to increase it. One of these efforts is the development of software reliability models (SRMs). SRMs have been studied for a long time as a model that predicts software reliability by using the number of software faults. Software failures can occur for several reasons, including independent software faults such as code errors and software hangs, as well as dependent cases where code errors lead to other software faults. Recently, due to the diversity of software operating environments, software faults are more likely to occur in a dependent manner, and, for this reason, they are likely to increase rapidly from the beginning and progress slowly to the maximum number thereafter. In addition, many large companies have focused on open-source software (OSS) development, and OSS is being developed by many users. In this study, we propose a new SRM that considers the number of finite faults and dependent faults, and examine the goodness-of-fit of a new SRM and other existing non-homogeneous Poisson process models based on the OSS datasets. Through numerical examples, the proposed model demonstrated a significantly better goodness-of-fit when compared to other existing models, and it also exhibited better results on the newly proposed integrated criteria.

Evolution of ice load prediction tools for hydrotechnical construction

The article provides an overview of the historical and contemporary developments in estimating ice loads on marine structures. It first underscores the significance of accurate ice load calculations for the safety and reliability of structures, and subsequently traces the evolution of ice load estimation techniques from past standards to current international standards and guidelines. The article then delves into the limitations of traditional methods for ice load estimation, which rely heavily on empirical data, before discussing the innovative and more precise discrete element method (DEM) for ice load estimation. The article outlines the benefits of using DEM over traditional methods and explores the diverse software options available for modelling ice loads on marine structures, including commercial and open-source alternatives. The article emphasizes the importance of staying up-to-date with the latest advancements in technology and techniques for ice load estimation, highlighting the crucial role that precise and reliable ice load estimation plays in ensuring the safety and dependability of marine structures in extreme environments. Overall, the article provides a comprehensive summary of the evolution of ice load estimation techniques for marine structures, from past norms to the contemporary use of DEM and advanced software packages. It underlines the necessity of continuous research to ensure that marine structures are constructed with the highest safety levels.

Analysis of Faults in Software Systems Using Tsallis Distribution: A Unified Approach

The identification of the appropriate distribution of faults is important for ensuring the reliability of a software system and its maintenance. It has been observed that different distributions explain faults in different types of software. Faults in large and complex software systems are best represented by Pareto distribution, whereas Weibull distribution fits enterprise software well. An analysis of faults in open-source software endorses generalized Pareto distribution. This paper presents a model, called the Tsallis distribution, derived using the maximum-entropy principle, which explains faults in many diverse software systems. The effectiveness of Tsallis distribution is ascertained by carrying out experiments on many real data sets from enterprise and open-source software systems. It is found that Tsallis distribution describes software faults better and more precisely than Weibull and generalized Pareto distributions, in both cases. The applications of the Tsallis distribution in (i) software fault-prediction using the Bayesian inference method, and (ii) the Goal and Okumoto software-reliability model, are discussed.

Quantifying Cybersecurity Effectiveness of Dynamic Network Diversity

The deployment of monoculture software stacks can have devastating consequences because a single attack can compromise all of the vulnerable computers in cyberspace. This one-vulnerability-affects-all phenomenon will continue until after software stacks are diversified, which is well recognized by the research community. However, existing studies mainly focused on investigating the effectiveness of software diversity at the building-block level (e.g., whether two independent implementations indeed exhibit independent vulnerabilities); the effectiveness of enforcing network-wide software diversity is little understood, despite its importance in possibly helping justify investment in software diversification. As a first step towards ultimately tackling this problem, we propose a systematic framework for modeling and quantifying the cybersecurity effectiveness of network diversity, including a suite of cybersecurity metrics. We also present an agent-based simulation to empirically demonstrate the usefulness of the framework. We draw a number of insights, including the surprising result that proactive diversity is effective under very special circumstances, but reactive-adaptive diversity is much more effective in most cases.

On the use of deep learning in software defect prediction

Context:Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming and hardly capture the semantic information reported in bug reporting tools. Deep learning (DL) techniques provide practitioners with the opportunities to automatically extract and learn from more complex and high-dimensional data. Objective:The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of the utilization of DL algorithms for SDP in the literature. Method:We systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results:Main highlights include: (1) most studies applied supervised DL; (2) two third of the studies used metrics as an input to DL algorithms; (3) Convolutional Neural Network is the most frequently used DL algorithm. Conclusion:Based on our findings, we propose to (1) develop more comprehensive DL approaches that automatically capture the needed features; (2) use diverse software artifacts other than source code; (3) adopt data augmentation techniques to tackle the class imbalance problem; (4) publish replication packages.

The model of SCRUM master selection using fuzzy sets

Problem. The complexity of software is constantly growing. Today a user is interested in software that is integrated into all aspects of their life. Creating a large number of diverse software applications is impossible without the formation of a software development team. Agile methodologies are used to develop most modern programs. The most common is the SCRUM methodology. Team working is managed by a SCRUM master, which is the interface between the owner and the project team. Therefore, choosing an effective SCRUM master is an urgent task. Aim The work aims to increase the efficiency of software development projects by developing the SCRUM master selection model using fuzzy sets, which will consider the individual characteristics and advantages of the SCRUM project team members. Methodology. The methods and principles of fuzzy mathematics are used. Decision-making methods in the conditions of fuzzy input data were used to develop a model for choosing a SCRUM master for a software development project. Results. Today, there are many variants of models for software development projects. The choice of one or another option depends on the features and requirements of the project. A fuzzy model for choosing a SCRUM master for managing a software development project has been developed. An example of work distribution between performers using the theory of fuzzy sets is given. Calculations were made in Microsoft Excel. Originality. The developed model allows choosing a SCRUM master in the conditions of fuzzy information about the individual preferences of the areas of the software development project team. Practical value. Using the proposed SCRUM master selection model for a software development project will improve project team communication and reduce the development time. In addition, the proposed model can be used to select a SCRUM master for other projects.