Software Architecture Research Articles

Architectural Design for Data Security in Cloud-based Big Data Systems

التصميم المعماري للنظام هو عملية إبداعية بعد كل شيء. المتطلبات غير الوظيفية وبنية البرامج لها علاقة قوية. يعد أمن النظام أحد الشواغل الكبيرة وهو حاجة ومتطلب غير وظيفي. يجب أن يعتمد الأسلوب والهيكل المعماري المختاران للنظام على متطلبات النظام غير الوظيفية. تُستخدم تقنيات البيانات الضخمة لتخزين كميات هائلة من البيانات التشغيلية في المجال العام. تم اقتراح مخططات هيكل وسلوك النظام التي تستخدم تصميمًا معماريًا آمنًا لنظام يعمل عبر السحابة. تم التحقيق في مكونات مختلفة من البيانات الضخمة في هذا العمل البحثي نظرًا لخطورة القضايا الأمنية التي تركز على الخصوصية والسرية. يعد تأمين البيانات عبر السحابة التي يتم الاستعانة بمصادر خارجية لها مصدر قلق متزايد. هناك مسؤولو قواعد بيانات ضارة من الخارج ومن الداخل يمكنهم الوصول إلى البيانات الخاصة والحساسة وتعديلها ، وبالتالي يمكن أن يكون مزود خدمة قاعدة البيانات مخادعًا. آليات الأمان مطلوبة بالضرورة لحماية البيانات الحساسة الموجودة على السحابات العامة. على مستوى التطبيق ، يضمن الحل المقترح خصوصية وسرية البيانات الموجودة على السحابة. تم إجراء اختبارات مكثفة ، وكانت نتائج هذا البحث كما يلي: كشفت النتائج المسجلة عن اختلافات ذات دلالة إحصائية في النص العادي ومحتويات الوسائط المتعددة (أي الصوت والفيديو) مع قياس الوقت بالمللي ثانية لمجموعات البيانات الصغيرة والمتوسطة والكبيرة.

RealPi2dDIC: A low-cost and open-source approach to In-situ 2D Digital Image Correlation (DIC) applications

In this current work an open-source 2D Digital Image Correlation (DIC) tool, RealPi2dDIC, is presented to monitor in-situ full field deformation and strain responses of structures during loading. This software is coded in Python3 and uses Raspberry Pi as the computation and Pi camera as the imaging device. This low-cost approach to in-situ DIC analysis enables its application in a broad field of research and industry problems where 2D strain field tensor is one of the principal interests of study. This code is provided with an interactive user interface (UI) making it user friendly and easy-to-use. Furthermore, RealPi2dDIC offers a simple software architecture which can be adopted, modified and extended to suit users’ purpose.

Research and implementation of a large-bandwidth real-time radar jamming simulator.

The electromagnetic environment faced by modern radar is becoming increasingly complex. One effective means to improve the performance of radar systems is testing in an anti-jamming ability test chamber, where the increased complexity has also led to higher performance requirements for radar jamming simulators. Based on the requirements for modern radar system testing, this paper presents a study of a large-bandwidth real-time radar jamming simulator and describes its overall design architecture; the simulator covers the L-Ku and Ka frequency bands and the instantaneous bandwidth is ≥2GHz, which means that the system is able to simulate 11 interference patterns. Synchronous control of the system is realized in 1ms through use of the reflection memory interrupt mechanism, the synchronous pulse signal mechanism, synchronous timing design, and a real-time control software architecture. An overall design scheme for real-time simulation of a radar target jamming echo is given and baseband signal processing resources are saved through information preprocessing, a large-capacity high-speed storage board is designed to improve the data reading speed, a multiphase filtering structure is used to achieve high sampling rates and save hardware resources, and a high-speed parallel computing method is used to improve computing efficiency; the actual measured baseband signal processing time is less than 500ns. Finally, a measurement platform is built, and the main interference patterns are verified through experimental measurements.

NEFELI: A deep-learning detection and tracking pipeline for enhancing autonomy in advanced air mobility

Efficient detection and accurate collision estimation for non-cooperative aerial vehicles are crucial for the realization of fully autonomous aircraft and Advanced Air Mobility (AAM). This paper introduces NEFELI, a machine learning software utilizing optical sensors to detect and track non-cooperative aerial vehicles. NEFELI's detector employs an enhanced YOLOv5-large model, strengthened with a sliced inference step to enhance detection capabilities for distant, diminutive objects. Furthermore, NEFELI introduces several innovations in its tracking component. A key advancement lies in the creation and utilization of the first large-scale re-identification (Re-ID) dataset of aerial objects. This dataset is used to train the deep learning appearance (Re-ID) model of the tracking module and integrates appearance information into the detection and tracking pipeline, resulting in more robust and reliable tracking performance. Moreover, the tracking model combines the deep learning appearance model with a Kalman Filter-based motion model to address the challenge of precisely tracking distant aerial objects. Notably, an extensive comparative analysis that was conducted showed that NEFELI outperforms state-of-the-art detection and tracking models in terms of Higher Order Tracking Accuracy (HOTA) metric, ID switches, and Association Accuracy (AssA) by a wide margin. A crucial aspect of this work is NEFELI's software architecture design, which enables efficient implementation on a low SWaP (Size, Weight, and Power) edge Graphic Processing Unit (GPU). To further showcase NEFELI's generalization capabilities and edge implementation performance, real-world flight experiments with small UAVs were carried out. The experimental results demonstrate NEFELI's ability to detect and track small UAVs at distances of up to 145 m in real-time speed of 6.7 fps.

Empowering Diversity in Education: A Web-Based Tool for Real-Time Sign Language Detection

People worldwide have severe hearing loss and there is little to no technological involvement to empower diversity and inclusion in the education system. This study aims to provide an understanding of the needs of the community of such individuals and a possible solution to the problem using current advances in artificial intelligence technologies. In particular, we will present a solution to the problem through a real-time sign language detection web application. First, the structural and functional aspects of the application are described, followed by the software architecture needs. The technological aspect of the application is entirely defined by expressing all the significant implementations of each programming language contained behind the software diagrams and architecture. This study will provide the latest technologies in the market combined with a well-planned and developed image detection application that is scalable, portable, maintainable, and efficient in empowering diversity in education. This technologically up-to-date application is delivered as a full-stack web application. The application produces results that waver between 85%-95% quality off detection; it consists of a bi-partite backend, one for creating and compiling models, namely Python, TensorFlow, and Jupyter Notebooks, and the other part held by Node.JS to power the front-end built with React and TensorFlow's JS. This study presents a fully developed web-based application that has the potential to impact and improve diversity in education significantly.

Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2′s development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.

Enhancing Non-Player Characters in Unity 3D using GPT-3.5

This case study presents a comprehensive integration process of OpenAI's GPT-3.5 large language model into Unity 3D to enhance non-player characters (NPCs) in video games and interactive applications. The study aims to develop an architecture and open source software framework that enables NPCs to engage in dynamic real-time interactions with players and other characters. The background and motivation for the study are provided, highlighting the existing limitations of traditional NPC programming and the potential of advanced natural language models such as GPT-3.5 to overcome these limitations. The methodology section outlines the step-by-step process, covering framework design and preparation, core architecture development, humanoid avatar integration and animation, and important feature extensions. The progression of framework design and implementation is described, emphasizing key architectural concepts, design patterns, and essential classes and interfaces. The results of the case study are discussed, focusing on the valuable insights gained and the implications for future advancements. Lessons learned from the integration process are shared, along with suggestions for potential improvements and directions for future research. This case study provides a practical resource for game developers and researchers interested in leveraging advanced natural language processing capabilities to create more immersive and interactive NPC experiences in Unity 3D environments.

Implementing an Agile-Driven Centralized System: A Key Strategy for Optimizing Stock Administration Efficiency

The stock administration program architecture involves user roles, such as managers, administrators, and staff, interacting with the system's components to access and process data. The system's development relies on hardware and application elements, including databases, front-end and back-end systems, code editing tools, and local servers. A use-case approach organizes stock administration, with diagrams showing users' interactions with the system. Administrators can manage stock by adding, modifying, or deleting items, while the system provides reports for analysis and decision-making. Staff members can view inventory, adjust stock levels, and receive alerts for low stock. Managers oversee stock requests, analyze reports, and manage staff accounts. A SUS questionnaire was administered to assess usability, revealing user perceptions based on ease of use, efficiency, and satisfaction. Comparing SUS scores across user roles helps identify usability challenges. High scores indicate that the system is user-friendly and effective, while lower scores suggest areas for improvement through training, redesign, or feature enhancements. Users reported high usability for ease of use, with support requirements scoring second highest. Staff ratings highlighted areas for improvement, such as stock level notifications, which can be addressed through redesign or training to enhance system usability.

Developing information technology for evaluating and enhancing application-layer DDoS attack detection methods

The subject matter of this article is the methods to detect distributed denial-of-service (DDoS) attacks at the Hypertext Transfer Protocol (HTTP) level with the purpose of justifying the requirements for creating software capable of identifying malicious web server clients. The goal of this article is to develop an information technology to evaluate the efficiency of DDoS attack detection methods, which will quantify their operating time, memory consumption, and approximate classification accuracy. In addition, this paper proposes hypotheses and a potential approach to improve existing application-layer DDoS attack detection methods with the intention of increasing their accuracy and identification speed. The tasks of this study are as follows: to analyse modern methods for detecting application-layer DDoS attacks; to investigate their features and short­comings; to develop a software system to assess DDoS attack detection methods; to programmatically implement these methods and experimentally measure their performance indicators, specifically: classification ac­curacy, operating time, and memory usage; to compare the efficiency of the investigated methods; to formulate hypotheses and propose an approach to improve existing methods and/or develop new methods based on the results obtained. The methods employed are abstraction, analysis, systematic approach, and empirical research. In particular, the datasets generated by DDoS utilities were processed using the synthetic minority oversampling technique (SMOTE) to balance them. Furthermore, the studied DDoS attack detection methods were implemented, including fitting the required parameters and training artificial neural network models for evaluation. The following results were obtained. The average classification accuracy, operating time, and random-access memory (RAM) consumption during Internet traffic classification were determined for six DDoS attack detection methods under the same conditions. This study has demonstrated that the development of a novel method to detect DDoS attacks at the HTTP level with enhanced accuracy and classification speed is strongly required. The experimental results demonstrate that the time series-based method exhibited the shortest operating time (1.33 ms for 5000 vectors), whereas the deep neural network-based method exhibited the highest average classification accuracy (ranging from 99.07% to 99.97%) and the lowest memory consumption (39.09 KB for 5000 vectors). Conclusions. In this study, a software system was developed to assess the average accuracy of DDoS attack classification methods and measure the computational resources utilized. The scientific novelty of the obtained results lies in the formulation of two hypotheses and a potential approach to the creation of a novel method for detecting DDoS attacks at the HTTP level, which will have both high classification accuracy and a short operating time to surpass previously studied analogues in these respects. The first hypothesis is based on the additional usage of HTTP request attributes during Internet traffic classification. The second hypothesis is to analyse a graph of user transitions between website pages. The article also superficially describes a potential approach that involves the implementation of the described hypotheses as well as the proposed software architecture of an application-layer DDoS attack detection system for the Kubernetes platform and the Istio framework, which addresses the issue of collecting web request parameter values for websites that use the cryptographically secured HTTPS protocol.

Bringing architecture-based adaption to the mainstream

Software architecture has been shown to provide an appropriate level of granularity for representation of a managed software system and reasoning about the impact of adaptation choices on its properties. Software architecture-based adaptability is the ability to adapt a software system in terms of its architectural elements, such as its components and their interfaces. Despite its promise, architecture-based adaptation has remained largely elusive, mainly because it involves heavy engineering effort of making non-trivial changes to the manner in which a software system is implemented. In this paper, we present Acadia—a framework that automatically enables architecture-based adaptation of practically any Java 9+ application without requiring any changes to the implementation of the application itself. Acadia builds on the Java Platform Module System (JPMS), which has brought extensive support for architecture-based development to Java 9 and subsequent versions. Acadia extends JPMS with the ability to provide and maintain a representation of an application’s architecture and make changes to it at runtime. The results of our experimental evaluation, conducted on three large open-source Java applications, indicate that Acadia is able to efficiently apply dynamic changes to the architecture of these applications without requiring any changes to their implementation.

Study of Human–Robot Interactions for Assistive Robots Using Machine Learning and Sensor Fusion Technologies

In recent decades, the potential of robots’ understanding, perception, learning, and action has been widely expanded due to the integration of artificial intelligence (AI) into almost every system. Cooperation between AI and human beings will be responsible for the bright future of AI technology. Moreover, for a perfect manually or automatically controlled machine or device, the device must perform together with a human through multiple levels of automation and assistance. Humans and robots cooperate or interact in various ways. With the enhancement of robot efficiencies, they can perform more work through an automatic method; therefore, we need to think about cooperation between humans and robots, the required software architectures, and information about the designs of user interfaces. This paper describes the most important strategies of human–robot interactions and the relationships between several control techniques and cooperation techniques using sensor fusion and machine learning (ML). Based on the behavior and thinking of humans, a human–robot interaction (HRI) framework is studied and explored in this article to make attractive, safe, and efficient systems. Additionally, research on intention recognition, compliance control, and perception of the environment by elderly assistive robots for the optimization of HRI is investigated in this paper. Furthermore, we describe the theory of HRI and explain the different kinds of interactions and required details for both humans and robots to perform different kinds of interactions, including the circumstances-based evaluation technique, which is the most important criterion for assistive robots.

Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education.

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology's complexity, costs, fabrication and implementation challenges often hinder engineers' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.

Parametric Design Method of Buildings Based on Convolutional and Deep Neural Networks

The application of AI technology in architectural software has brought various innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them, and analyzes the application of these technologies in architectural image recognition, generation, design optimization and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design. The application of AI technology in architectural software has brought numerous innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them; it also analyzes the application of these technologies in architectural image recognition, generation, design optimization, and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design.

A group decision-making model for architectural programming in megaprojects

PurposeDue to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.Design/methodology/approachThis study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.FindingsThis study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.Originality/valueAPMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

I-PI 3.0: A flexible and efficient framework for advanced atomistic simulations.

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.

Forte: A suite of advanced multireference quantum chemistry methods.

Forte is an open-source library specialized in multireference electronic structure theories for molecular systems and the rapid prototyping of new methods. This paper gives an overview of the capabilities of Forte, its software architecture, and examples of applications enabled by the methods it implements.

Architectural Software Preferences in Correlation to Architectural Skills

Architectural Software Preferences in Correlation to Architectural Skills

Client-Server Code Mobility at Runtime

Application logic is inherently distributed between client and server due to the fundamental Client/Server architecture of the Web. The individual distribution is specified at design time and remains unchanged stable, preventing individual load distribution between clients and server at runtime. Dynamic code mobility at runtime, in contrast, allows to balance the needs of users, through increased responsiveness, and software providers, through better resource usage and cost reductions. Enabled by WebAssembly, the Web ecosystem recently provides the technological foundation for relocating code units during runtime. However, leveraging these capabilities to enhance web applications with dynamic code migration presents challenges for web engineers. In response, we propose an innovative distributed Client/Server software architecture for web applications. This architecture facilitates the dynamic migration of code at runtime, and addresses the technical challenges like dependency management, control and data flow distribution, communication, and interfaces. This novel software architecture serves as a reference for web engineers aiming to enrich their web applications with dynamic code mobility. Additionally, it contributes to the ongoing reevaluation of the Web ecosystem in light of the widespread adoption and standardization of WebAssembly across major browsers. Through experimentation in four scenarios, we demonstrate the feasibility of implementing this architecture, its negligible impact on performance and the optimization potential for individual code distributions across client and server.

Optimizing multi-tenant database architecture for efficient software as a service delivery

Optimizing multi-tenant database architecture for efficient software as a service delivery

Smart ArM: a customizable and versatile robotic arm prosthesis platform for Cybathlon and research

BackgroundIn the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants.MethodsTo meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics.ResultsThe Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity.ConclusionThese application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.