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When we tell stories, we play with them. Theme, character, setting, and plot are some of the main sites of this style of play. Fresh perspectives on these elements allow them to be combined and interpreted and new ways, inspiring further play. Interactive narrative in computation has explored this idea from a number of perspectives, often running into the problem of how to invite and incorporate a naive audience member into a story. In this dissertation, I discuss my work re-examining the assumptions of interactive storytelling, drawing from theatrical practices to create AI systems of expressive performance, their use and impact on narrative design, and ways of supporting the development of experiences built around the idea of players as performers. This combination of design theory, practice, and evaluation blend together into \textit{computational theater}: a new approach to creating interactive narratives and AI driven character behavior rooted in the aesthetics of theatrical performance. Through this approach, I illustrate how performance expands the ways a player can interact with and influence narratives through play with AI controlled characters as well as the resulting implications for the development of AI-based interfaces and simulations.

Modular software applications are developed based on a software design technique that emphasizes separating the program's functionality into independent, interchangeable components. Each component contains everything necessary to provide certain functionality. Building software systems using modules introduces many benefits, including improving software quality, easier maintainability, and better scalability, especially for large, complex, and long-running software systems. Although a software system's architecture is often conceptualized in terms of high-level constructs, e.g., software components, connectors, and interfaces, programming languages usually provide low-level constructs, e.g., classes, methods, and variables. To bridge this gap, many widely used programming languages, such as Java and C++, have recently incorporated the notion of module or component. The increasing support of modularization in the modern programming languages, although offering many benefits, introduces plenty of new challenges in all phases of software development and execution, including at build-time, module installation-time, and run-time. Hence, the need for effective validation and verification techniques has increased more than ever. This dissertation proposes to advance validation and verification of modular applications through investigating the static and dynamic behavior of modular applications and introducing automatic validation, and verification techniques to (1) detect and repair architectural inconsistencies at both build- and run-time, and (2) test dynamic delivery failures at module installation-time.All conducted experiments on real-world subject apps corroborate the effectiveness and efficiency of the proposed approaches and their ability to validate and verify the behavior of modular software applications at all different phases of their development and execution.

Microservice architecture has become widely-used in industry, with tech giants like Amazon, Twitter, and LinkedIn leveraging microservices to evolve their web-scale applications. Microservice architecture brings benefits such as scalability and technological heterogeneity, although at a cost of complexity. A number of microservice patterns have been proposed to address this cost. This thesis explores the use of microservice patterns in recovered microservice architectures; however, there have been few attempts at recoveries of microservice systems. The contributions of this thesis are: 1. A definition of microservices as an architectural style, 2. A recovery technique for orchestrated systems which has been applied to three benchmark systems, and 3. A microservice pattern identification technique for five patterns, and applying it to the three recovered systems. In our results, 4 of the 5 patterns in all three benchmark systems were successfully identified.

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.

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.

Java is one of the most widely used programming languages. However, the absence of explicit support for architectural constructs, such as software components, in the programming language itself has prevented software developers from achieving the many benefits that come with architecture-based development. To address this issue, Java 9 has introduced the Java Platform Module System (JPMS), resulting in the first instance of encapsulation of modules with rich software architectural interfaces added to a mainstream programming language. The primary goal of JPMS is to construct and maintain large applications efficiently—as well as improve the encapsulation, security, and maintainability of Java applications in general and the JDK itself. A challenge, however, is that module declarations do not necessarily reflect actual usage of modules in an application, allowing developers to mistakenly specify inconsistent dependencies among the modules. In this paper, we formally define 8 inconsistent modular dependencies that may arise in Java-9 applications. We also present Darcy, an approach that leverages these definitions and static program analyses to automatically (1) detect the specified inconsistent dependencies within Java applications and (2) repair those identified inconsistencies. The results of our experiments, conducted over 38 open-source Java-9 applications, indicate that architectural inconsistencies are widespread and demonstrate the benefits of Darcy in automated detection and repair of these inconsistencies.

Distinct technological trends seriously influence the modern software architectures. In this paper, four different software architectures and framework combinations were generally compared. The basis for the analysis is the developer's productivity. In a 3 year-long research and development project, a real-world telemedicine application was efficiently implemented four times with various software architectures and architectural patterns. More than 5,000 person-hours were spent on carrying out them. At present, a unique dataset is available, which provides the opportunity to compare the cost of spent person-hours in different approaches. The goal of this research is to describe the measurement approach, the dataset and the applied architectures considering the software developer's productivity.

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 paper presents a redundant multi-object detection method for autonomous driving, exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to detect different obstacles. These sensors are used for distinct perception pipelines considering a custom hardware/software architecture deployed on a self-driving electric racing vehicle. Consequently, the creation of a local map with respect to the vehicle position enables development of further local trajectory planning algorithms. The LiDAR-based algorithm exploits segmentation of point clouds for the ground filtering and obstacle detection. The stereocamerabased perception pipeline is based on a Single Shot Detector using a deep learning neural network. The presented algorithm is experimentally validated on the instrumented vehicle during different driving maneuvers.

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.

In traditional classroom education the teacher can easily perceive or obtain the engagements of the students by observing them. Distance education is affected by the absence of such a feedback coming from expressions and behaviours of the students attending the lesson. Aiming at reducing the gap between these two learning modalities, the proposed system analyses student videos recorded by the cameras available on the laptops by which they are attending the lecture. This approach provides aggregated information on didactic efficacy to the teacher, avoiding the need of sharing student video recordings during the lecture. The teacher is therefore supported during the oral exposition of the lecture. The approach proposed in this study has been conceived as a software architecture running on background and locally on students’ personal computers. No sensitive data are shared over the network. It has been evaluated in two experimental sessions dedicated to a sensitivity preliminary evaluation of the proposed instrument and to the assessment of its didactic usefulness by volunteers, students and industrial employees. User evaluation reports, both on student and teacher side, a positive feedback. The discussion can bring to insights and new considerations about learning in general, which is nowadays so significantly forced to change due to COVID-19 pandemic and it could be intended to change even more in the direction of distance learning in the future.

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.

The cognitive network controller (CNC) is a brain-inspired computing architecture that has been applied to the decentralized outbound link selection for data bundles in challenged networks. To this end, cognitive entities perceive the environment, reason using spiking neural networks (SNN), and decide routing actions that attempt to optimize pre-assigned performance goals. In this paper, a software architecture and SoC/FPGA implementation are proposed that enable decoupling the SNN computation tasks from the network gateways allowing the provision of accelerated CNCs as a network service to multiple network gateways. The design was experimentally evaluated using a 10-node network testbed and a Zynq Z7020 (PYNQ-Z2) SoC/FPGA board, which show the feasibility of computing the SNN state faster-than-real time despite the shared use of resources and the network communication overhead involved in remotely accessing the CNC service.

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.

Android is widely used for the development and deployment of autonomous and smart systems, including software targeted for IoT and mobile devices. Security of such systems is an increasingly important concern. Although Android is the predominant mobile platform, it is also the most targeted platform by malware authors resulting in millions of malicious apps distributed in numerous app stores. Android relies on a permission model to secure the system's resources and apps. In Android, since the permissions are granted at the granularity of apps, and all components in an app inherit those permissions, an app's components are over-privileged, i.e., components are granted more privileges than they actually need. Systematic violation of least-privilege principle in Android is the root cause of many types of Inter-Component Communication (ICC) attacks that can lead to serious security and privacy risks.Due to the increasing use of code obfuscation in Android apps, the current security mechanisms for Android apps, both static and dynamic analysis approaches, are insufficient for detection and prevention of the increasingly dynamic and sophisticated security attacks.Static analysis approaches suffer from false positives whereas dynamic analysis approaches suffer from false negatives. Moreover, they all lack the ability to efficiently analyze systems with incremental changes---such as adding/removing apps, granting/revoking permissions, and dynamic components' communications. Each time the system changes, the entire analysis needs to be repeated, making the existing approaches inefficient for practical use.To mitigate these issues, this dissertation presents a novel self-protecting Android software system that automatically determines and continuously maintains the least-privilege architecture of an Android system, incrementally and efficiently analyzes its security posture, and dynamically enforces the maintained least-privilege architecture at runtime. The approach, entitled SALMA, protects the system against ICC attacks at all times in spite of changes at runtime.The least-privilege architecture limits the privileges granted to apps without the need to modify them or breaking their functionalities. Static program analysis techniques have been utilized to extract the exact privileges each component needs for providing its functionality.A Multiple-Domain Matrix representation of the system's least-privilege architecture is then kept in sync with the running system to reason about it at runtime. Every time the system changes, SALMA determines (1) the impacted part of the system, and (2) the subset of the security analyses that need to be performed, thereby greatly improving the performance and the scalability of the approach.All conducted experiments on hundreds of real-world apps corroborate the scalability and efficiency of the proposed approach in reducing the attack surface of Android systems as well as its ability to detect and prevent security attacks at runtime with minimal disruption.