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Functional and nonfunctional characteristics of software systems are defined by their architecture. Therefore, research streams such as Internet-of-Things (IoT) or component-based software engineering provide researchers and practitioners with construction guidelines for selected architectural characteristics. Current systems can be categorized in delivering services to the user and being engineered in a smart way. For example, services being provided by IoT-Systems must fulfill users’ goals in a highly dynamic and ad-hoc way. Consequently, this survey aims at answering various research questions regarding the methodical composition of system components and services. Furthermore, new research opportunities are sketched that should be tackled to make the scientific progress available to practitioners. Based on a systematic literature review from a software architecture point of view, in this paper we identify 75 primary studies for domain-specific IoT component composition approaches and architectures. Initial results show that current integration approaches mainly focus on performance evaluation of their integration solutions, which may be too narrow for fulfilling user goals by utilizing of IoT architectures.

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

Digital engineering is increasingly introduced for managing and supporting the development of systems for space. However, few academic teams have the competency needed to manage projects using digi...

Background Millions of people throughout the world suffer from parasite infections. Traditionally, technicians use manual eye inspection of microscopic specimens to perform a parasite examination. However, manual operations have limitations that hinder the ability to obtain precise egg counts and cause inefficient identification of infected parasites on co-infections. The technician requirements for handling a large number of microscopic examinations in countries that have limited medical resources are substantial. We developed the helminth egg analysis platform (HEAP) as a user-friendly microscopic helminth eggs identification and quantification platform to assist medical technicians during parasite infection examination. Methods Multiple deep learning strategies including SSD (Single Shot MultiBox Detector), U-net, and Faster R-CNN (Faster Region-based Convolutional Neural Network) are integrated to identify the same specimen allowing users to choose the best predictions. An image binning and egg-in-edge algorithm based on pixel density detection was developed to increase the performance. Computers with different operation systems can be gathered to lower the computation time using our easy-to-deploy software architecture. Results A user-friendly interface is provided to substantially increase the efficiency of manual validation. To adapt to low-cost computers, we architected a distributed computing structure with high flexibilities. Conclusions HEAP serves not only as a prediction service provider but also as a parasitic egg database of microscopic helminth egg image collection, labeling data and pretrained models. All images and labeling resources are free and accessible at http://heap.cgu.edu.tw . HEAP can also be an ideal education and training resource for helminth egg examination.

Electrical neuromodulation is an approved therapy for a number of neurologic disease states, including Parkinson's disease (PD), Obsessive Compulsive Disorder, Essential Tremor, epilepsy and neuropathic pain. Neuromodulatory strategies are also being piloted for an increasing number of additional indications, including Major Depressive Disorder, Dystonia, and addiction. The development of implantable devices capable of both neural sensing and adaptive stimulation may prove essential for both improving therapeutic outcomes and expanding the neuromodulation indication space. Nevertheless, an increasingly fragmented device ecosystem forces researchers and therapy developers to customize and reinvent data visualization, clinician engagement, and device control software to support individual clinical studies. Each hardware platform provides a unique software interface to the implanted neurostimulator, making pre-existing code from prior studies difficult to leverage for future work ‐‐ a hindrance that will expand as device technology diversifies. Here, we envision, detail, and demonstrate the use of a novel software architecture, OMNI, that accelerates neuromodulation research by providing a flexible, platform- and device-agnostic interface for clinical research and therapy development.

Self-adaptation gives software systems the ability to adjust their behavior or structure in changing environments such as the health software domain However, adaptability significantly complicates software implementation and brings great challenges to software engineering This paper proposes an Adaptive Healthcare Software Architecture (AHSA), which is based on international health standards and offers at-runtime adaptation Three components (Dynamic Linker, Runtime Locator, and Autonomous Component) have been specified to compose AHSA A tool called AdaptiveHIS has also been developed to dynamically generate AHSA components, and this generation is exemplified through an algorithm Within two real-world COVID-19 pandemic healthcare scenarios, an assessment compared the adaptability of AHSA and other software architectures built using state-of-art tools Results based on the level of system adaptability metric respectively showed that AHSA offered 20% and 72% more adaptability in the two real-world scenarios IEEE

This report provides suggestions for documenting service-oriented architecture-based systems based on the Views & Beyond (V&B) software documentation approach. The V&B documentation approach is a lightweight and flexible approach to documenting software architecture developed by Carnegie Mellon University’s Software Engineering Institute. This report also includes an overview of several well-known service-oriented architecture (SOA) documentation challenges and suggestions for tailoring and augmenting the V&B approach to address those challenges. The author hopes that the suggestions presented in this report will help SOA developers to avoid some of the common documentation pitfalls and produce higher quality SOA documentation.

This project aims to adapt existing software engineering methods to ensure that the final products, which are being developed through this method, will be ethically sustainable.

We review the background, classification, specifications, and related aspects of using smart contracts in blockchain services and introduce a framework for generating a general software architecture in the context of contract-oriented programming.

The rapid development of space technology has made it increasingly important to use space robots for on-orbit services. Space crawling robots can perform extravehicular condition monitoring, spacecraft fault location and diagnosis, repair and maintenance by carrying different payloads and traversing the spacecraft surface, which has received wide attention from researchers. A prototype of space crawling robot has been designed in this work, which is a quadrupedal insect-like configuration with integrated bionic adhesion material on the bottom of the four feet, and can walk on the satellite surface in a gravity-free space environment. The design and implementation of the crawling robot control system is carried out based on Robot Operating System, including hardware system construction and software architecture design. The tripod gait was also designed to enable the robot to crawl more stably in space. At last, the basic capabilities of the designed space crawling robot are tested in the ground environment, and the results demonstrate the robot’s body control capability, omnidirectional walking capability, and obstacle crossing and avoidance capability.

A software development process comprises all the steps necessary to produce a software product. This research proposes a new process for developing augmented reality serious games (ARSGs), which comprises three phases: analysis, configuration, and generation. The analysis phase involves examining the standard project structure of a typical serious game to identify the applicable actions to which AR content can be added. The configuration phase involves configuring the AR features and the AR library to be integrated into the game structure. Finally, the generation phase entails inserting the AR code in the game structure, as well as all the files necessary to in-corporate AR features in the game. Our process allows generating ARSGs step by step, both easily and rapidly. To implement this process, we propose an ARSG development software architecture. The underlying assumption of this architecture is that software development tools are essential validation elements of the software generation process. By implementing our ARSG development process through a software architecture, we provide users with a formal, automated method for creating ARSGs. The ARSG development process and the architecture, collectively called ZeusAR, are not dependent on specific software development technologies and/or programming languages. To perform a proof-of-concept of our process, we developed an ARSG generator tool based on the proposed architecture and conducted a case study in which geometry ARSGs are developed to help high school students learn about geometric shapes and their properties. We conducted a qualitative evaluation of the ZeusAR tool through a System-Usability-Scale (SUS) based survey, which was administered to a group of geometry teachers to evaluate the tool’s characteristics, such as ease of use and ease of configuration. Additionally, a group of software developers and professors assessed the performance of the ZeusAR tool in terms of game development time.

The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.

ICS (industrial control system) has gone through constant evolution in both hardware and software with the growing complexity of control applications. The generalization for these control systems has been a trend, and the hardware platform for ICS is getting multifunctional. Therefore, it is necessary to apply methods and technologies from the general software domain to handle this. CBSE (component-based software engineering) can promote reusability during software development and decouple the system with scalability. Existing component-based methods for ICS lack support for advanced function with modern technologies, and traditional software architectures are not flexible enough and usually vendor specific, which brings difficulties to system integration. In this paper, we propose an Open Software Architecture for Industry based on a novel component model. The motivation is to provide a component-based method for ICS and an open framework for various application parts and multi-vendor cooperation. We concern about three aspects including reusability, openness, and flexibility. Meanwhile, a flexible runtime architecture for the component model that supports isolation and real-time performance also matters. We also implement a simple real-life case based on the component model and software architecture we design and evaluate real-time and communication performance.

The increase in the volume of passenger transportation in megalopolises and large urban agglomerations is efficiently provided by the integration of urban public transit systems and city railways. Traffic management under those conditions requires creating intelligent centralised multi-level traffic control systems that implement the required indicators of quality, comfort, and traffic safety regarding passenger transportation. Besides, modern control systems contribute to traction power saving, are foundation and integral part of the systems of digitalisation of urban transit and the cities. Building systems solving the traffic planning and control tasks is implemented using algorithms based on the methods of artificial intelligence, principles of hierarchically structured centralised systems, opportunities provided by Big Data technology. Under those conditions it is necessary to consider growing requirements towards software as well as theoretical design and practical implementation of network organisation.This article discusses designing architecture and shaping requirements for developed applications and their integration with databases to create a centralised intelligent control system for the urban rail transit system (CICS URTS). The article proposes the original architecture of the network, routing of information flows and software of CICS URTS. The routing design is based on a fully connected network. This allows to significantly increase the network bandwidth and meet the requirements regarding information protection, since information flows are formed based on the same type of protocols, which prevents emergence of covert transmission channels. The implementation of the core using full connectivity allows, according to the tags of information flows, to pre-form the routes for exchange of information between servers and applications deployed in CICS URTS. The use of encrypted tags of information flows makes it much more difficult to carry out attacks and organise collection of information about the network structure.Platforms for development of intelligent control systems (ICS), which include CICS URTS, high computing power, data storage capacity and new frameworks are becoming more available for researchers and developers and allow rapid development of ICS. The article discusses the issues of interaction of applications with databases through a combination of several approaches used in the field of Big Data, substantiates combination of Internet of Things (IoT) methodology and microservice architecture. This combination will make it possible to single out business processes in the system and form streaming data processing requiring operational analysis by a human, which is shown by relevant examples.Thus, the objective of the article is to formalise the principles of organising data exchange between CICS URTS and automated control systems (ACS) of railway companies (in our case, using the example of JSC Russian Railways), URTS services providers, and city government bodies, implement the developed approaches into the architecture of CICS URTS and formalise principles of organisation of microservice architecture of CICS URTS software. The main research methods are graph theory, Big Data and IoT methods.

The concepts of service-oriented computing (SOC) have previously been used in the embedded systems domain, mostly at the device level in ad hoc manners to achieve advantages of device integration. However, SOC has not been used for the development of embedded software systems (ESS), i.e., software controlling the embedded devices. To fill this gap, a process model is proposed in this article that allows the systematic development of ESS based on SOC concepts. The proposed process consists of analysis and design phases of embedded software development. The analysis phase is concerned with the collection of system information and preparation for system design. Based on this, the service-based software architecture is developed in the design phase. The effectiveness of the proposed process model is demonstrated through its application in the Smart Home case study.

We address the relationship between Agile and software project management (SPM), discussing the extent to which agile practices help to reduce or avoid different SPM antipatterns. Our results may be particularly useful to organizations that are becoming agile.

Purpose The diagnostic reference level (DRL) has been established to optimize the diagnostic methods and reduce radiation dose during radiographic examinations. The aim of this study was to present a completely new solution based on Cloud-Fog software architecture for automatic establishment of the DRL values during dental cone-beam computed tomography (CBCT) according to digital imaging and communications in medicine (DICOM) structured reports. Methods and Materials A Cloud-Fog software architecture was used for automatic data handling. This architecture used the DICOM structured reports as a source for extracting the required information by fog devices in the imaging center. These devices transferred the derived information to the cloud server. The cloud server calculated the value of indication-based DRL in dental CBCT imaging based upon the parameters and adequate quantities of the absorbed dose. The feedback of DRL value was continuously announced to the imaging centers in 6 phases. In each phase, the level of the dose was optimized in imaging centers. Results The DRL value was established for 5-specific indications, including third molar teeth (511 mGy.cm2), implant (719 mGy.cm2), form and position anomalies of the tooth (408 mGy.cm2), dentoalveolar pathologies (612 mGy.cm2), and endodontics (632 mGy.cm2). The determination of the DRL value in each phase revealed a downward trend until stabilization. Conclusion The new solution presented in this study makes it possible to calculate and update the DRL value nationally and automatically among all centers. Also, the results showed that this approach is successful in establishing stabilized DRL values.

This work starts with a background study of serendipity, its meaning in the context of information discovery, its interrelation with bibliographic relationships, and the technical possibilities for practical implementation of a relationships based visual navigation in a library discovery interface to achieve serendipitous resource discovery. The methodology for developing the prototype consists of two components – theoretical base and practical steps. The theoretical framework includes an indepth study of the concept of bibliographic relationships as proposed by experts, as reflected in bibliographic data models and as included in bibliographic formats and metadata schemas. The practical sides deal with the application of the theoretical framework in designing a prototype that, in addition to other typical retrieval features, supports visual navigational facility driven by bibliographic relationships. The components, tools and standards of the entire software architecture are all open source and open standards.

Pervasive Healthcare systems are breeding rapidly and distributed systems such as fog, cloud, and IoT have made it possible for these systems to scale extensively with a certain need to sustain interoperability, reliability, availability, and response time. Notwithstanding those remarkable efforts that have been conducted for the architecting of such systems, there is a certain obligation toward the design of an architecture for the domain to constitute the aforementioned requirements. Our goal in this paper is to present a software architecture for IoT-based healthcare systems to address the above-mentioned non-functional requirements that include best practices of IoT, Fog, and Cloud planes. The proposed architecture is illustrated with 4 + 1 views and graph transformation is used to transform the models and expressing operational semantics. The architecture tradeoff analysis method is applied to evaluate different scenarios and both formal verifications and trade-off analysis proved the eligibility and competence of the proposed architecture.

Context: Evaluating software architectures in uncertain environments raises new challenges, which require continuous approaches. We define continuous evaluation as multiple evaluations of the software architecture that begins at the early stages of the development and is periodically and repeatedly performed throughout the lifetime of the software system. Numerous approaches have been developed for continuous evaluation; to handle dynamics and uncertainties at run-time, over the past years, these approaches are still very few, limited, and lack maturity. Objective: This review surveys efforts on architecture evaluation and provides a unified terminology and perspective on the subject. Method: We conducted a systematic literature review to identify and analyse architecture evaluation approaches for uncertainty including continuous and non-continuous, covering work published between 1990–2020. We examined each approach and provided a classification framework for this field. We present an analysis of the results and provide insights regarding open challenges. Major results and conclusions: The survey reveals that most of the existing architecture evaluation approaches typically lack an explicit linkage between design-time and run-time. Additionally, there is a general lack of systematic approaches on how continuous architecture evaluation can be realised or conducted. To remedy this lack, we present a set of necessary requirements for continuous evaluation and describe some examples.

Software architectural designs are usually changed over time to support emerging technologies and to adhere to new principles. Architectural migration is an important activity that helps to transform the architectural styles applied during a system’s design with the result of modernising the system. If not performed correctly, this process could lead to potential system failures. This article presents an automated approach to refactoring architectural design and to planning the evolution process. With our solution, the architectural design can be refactored, ensuring that system functionality is preserved. Furthermore, the architectural migration process allows the system to be safely and incrementally transformed. We have evaluated our approach with five real-world software applications. The results prove the effectiveness of our approach and identify factors that impact the performance of architectural verification and migration planning. An interesting finding is that planning algorithms generate migration plans that differ in term of their relative efficiency.

Software product lines (SPL) dynamic configuring process could use methods for recommendation system (RS) elaboration. An overview and analysis of such methods was done in this paper. SPL represent a set of software systems that have common and variable functional components and use a set of paradigms and methods for development. In the classical static SPL the process of configuring performed before executing and performing in the operation environment (OE), in contrast dynamic software product lines performs after executing in the OE. Through the use of which it is possible to customize software solutions in accordance with the needs of end users. The following possible methods to build RS were considered: clustering, Markov decision-making process, matrix factorization. According to the review of the intelligent RS method development and researching of the functionalities of such systems in some open-source projects it was proposed to use N-dimensional context-dependent tensor factorization method and CARSkit tool system. Functional requirements and software architecture of the RS were developed. It allows to automatize software components configuration in the „Smart Home” (SH) systems that could be implemented with CARSkit software toolkit and algorithms implemented with programming language Python. This implementation allows to build a process for tracking changes in the external environment and transfer information to the SH system and, after analyzing the input data, process it in the RS to track changes in the context information. In the future research some additional quantitative experiments will be performed considering the specifics of the SH systems, additionally quantitative metrics will be used for efficiency assessment of the tensor factorization algorithms to predict the dynamic configurations of software components in these systems. Keywords: recommendation system, software product line, variability, dynamic configuration, architecture.

Context. For computer modeling of complex objects and phenomena of various nature, in practice, the numerical finite element method is often used. Its software implementation (especially for the study of new classes of problems) is a rather laborious process. The high cost of software development makes the development of new approaches to improving the efficiency of programming and maintenance (including the addition of new functions) urgent. Objective. The aim of the work is to create a new effective architecture of programs for finite element analysis of problems in mathematical physics, which makes it easy to expand their functionality to solve new classes of problems. Method. A method for developing programs for finite element analysis using generalized programming is proposed, which makes it possible to significantly simplify the architecture of the software and make it more convenient for maintenance and modification by separating algorithms and data structures. A new architecture of classes that implement finite element calculation is proposed, which makes it possible to easily expand the functionality of programs by adding new types of finite elements, methods for solving systems of linear algebraic equations, parallel computations, etc. Results. The proposed approach was implemented in software as a class library in C ++. A number of computational experiments have been carried out, which have confirmed its efficiency in solving practical problems. Conclusions. The developed approach can be used both to create general-purpose finite element analysis systems with an open architecture, and to implement specialized software packages focused on solving specific classes of problems (fracture mechanics, elastomers, contact interaction, etc.).

Microservices and Big Data are renowned hot topics in computer science that have gained a lot of hype. While the use of microservices is an approach that is used in modern software development to increase flexibility, Big Data allows organizations to turn today’s information deluge into valuable insights. Many of those Big Data architectures have rather monolithic elements. However, a new trend arises in which monolithic architectures are replaced with more modularized ones, such as microservices. This transformation provides the benefits from microservices such as modularity, evolutionary design and extensibility while maintaining the old monolithic product’s functionality. This is also valid for Big Data architectures. To facilitate the success of this transformation, there are certain beneficial factors. In this paper, those aspects will be presented and the transformation of an exemplary Big Data architecture with somewhat monolithic elements into a microservice favoured one is outlined.

The purpose of the article is to study, analyze and consider general problems and prospects of using ontologies when modelling decision-making processes in intelligent systems when analyzing the risks of the innovation and investment sphere. The research methodology lies in methods of the basic concepts semantic analysis of a given subject area (innovation and investment projects and management processes for the implementation of these projects). The article discusses the existing approaches to modelling decision-making processes in the analysis of risks in the innovation and investment sphere. The novelty of the research is the intellectualization problems solving of processes in the innovation and investment sphere on the basis of formal ontological models. Conclusions. The practical value of the presented results lies in the development and use of knowledge management system components for identifying and predicting problem situations in innovation and investment projects. The proposed integrated ontology can be used in the management of innovation and investment projects in various subject areas since it contains classes of concepts that have the status of a project activity standard. The developed ontological model makes it possible to develop software architecture for an intelligent decision support system, develop metadata and build a set of interrelated thesauri to support the semantics of end-user requests.

We currently find ourselves in a surprising situation: we understand software architecture fairly well, but we find it difficult to put it into practice. This is because most software developers work within iterative processes that focus attention on what’s new, and because factory-inspired metaphors encourage ever-quicker movement from feature requests to deployed code. How did that happen? Explores changes in software development management and reports on where it is headed in the near future.

Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. A fall detection system aims to detect a fall as soon as it occurs, therefore issuing an automatic assistance request. Wearable embedded sensors are emerging as the most viable solution for continuous monitoring since they are more effective, less intrusive and less expensive than other systems. Tailoring a deep learning method to the requirements of microcontrollers entails matching very stringent constraints in terms of both memory and computational power. In addition, datasets acquired with wearable devices are relatively scarce and not necessarily devised for supervised learning. In this work, we discuss the design of a software architecture based on recurrent neural networks which can be effective for fall detection while running entirely onboard a wearable device. The well-known and publicly-available SisFall dataset was adopted and extended with fine-grained temporal annotations to cope with the supervised training of recurrent neural networks. We then show that an appropriate process of architectural minimization together with accurate hyperparameters selection leads to a workable model which compares favorably with other detection techniques. The embedding of the resulting architecture has been validated using a state-of-art hardware device.

This letter introduces GARMI, a service robotics platform conceptualized with a focus on assisting elderly at home. GARMI is designed to provide support with household tasks, as an avatar for tactile-enabled communication and as an interface for telemedicine and emergency assistance. Its unique humanoid design features a sensor-equipped multi-modal head designed for natural human-machine communication as well as a whole-body torque-control interface for safe physical human-machine interaction. GARMI's modular software architecture and distinctive whole-body control scheme allows multimodal dynamic coupling. Additionally, every system component can actively produce as well as sense forces and can thus serve as a haptic feedback interface when interacting with its environment and users. Furthermore, GARMI is the first mobile humanoid designed with specialized use-inspired avatar stations: PARTI for dual-arm-based exoskeleton-like remote-control with force-feedback and MUCKI for transparent remote doctor-patient interaction with both audiovisual and safe haptic feedback. The specialized software architecture allows for rapid prototyping and field-testing of new behaviors for telemedicine, multi-modal interaction and autonomous physical and service assistance. Our first results reveal the potential of our use-driven force-based whole-body control mobile humanoid for daily living and telemedicine scenarios in an elderly care research facility.

Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts involving multiple stakeholders. Together with an automotive supplier, we analyzed how technology, business, and organizational factors raise the need for flexible traceability. We present how traceability can be evolved in the development lifecycle, from early elicitation of traceability needs to the implementation of mature traceability strategies. Moreover, we shed light on how traceability can be managed flexibly within an agile team and more formally when crossing team borders and organizational borders. Based on these insights, we present requirements for flexible tool solutions, supporting varying levels of data quality, change propagation, versioning, and organizational traceability., 9 pages, 3 figures, accepted in IEEE Software