Modeling Emergent Behavior for Systems‐of‐Systems

Differential privacy is emerging as an effective solution to achieve privacy protection for the Artificial Intelligence neural network (ANN). However, not only matrix calculations of a neural network but also random noise injection mechanisms for differential privacy consume large power and resources. Traditionally, most privacy protection technologies are software technologies using von Neumann architecture and hardware with extra noise generation circuit unit. In this paper, a memristor based crossbar in-memory computing system is proposed to enable energy efficient privacy preserving technology in ANN. We utilize inherent cycle-to-cycle variations of memristors and apply the proposed variation-based pulse pair method during the weight update process. As a result, the proposed methods realize a machine learning system with privacy protection and show up to 29.24% recognition accuracy improvement with various privacy budget ε.

Systems of Systems (SoS) are complex systems composed of managerially and operationally independent constituent systems (CS). Smart cities are examples of SoS. However, these types of systems impose challenges to traditional software architecture such as highly evolutionary architecture due to the evolution of individual CS and emergent behaviors that results from the interoperability of CS. Executable Models (ExM) are a class of models that can be executed and that can assist on architectural design of SoS. By using them, architects can predict the SoS structure and behavior by visualizing and simulating the SoS still at design-time. On the other hand, as any other emergent technologies, it suffers with absent scientific evidences of its benefits, mainly in industrial context. The main contribution of this master thesis project intends to be the presentation of evidences about the use of ExM to solve problems in the SoS software architecture design. For achieving such purpose, this project is structured in well-defined steps: (i) a systematic mapping study, (ii) elaboration of a conceptual map derived from the mapping study, (iii) a survey to obtain perceptions from software engineering professionals on the use of ExM in the context of engineering simple systems and (iv) a new survey expanding the previous study and to obtain perceptions from software engineering professionals on the use of ExM in the context of architectural design of SoS. Preliminary results reveal that ExM have been reported in diverse SoS domains, offering a dynamic and interactive view to the SoS. We also found that software engineering professionals see ExM as suitable solution for dealing with complex and critical systems as SoS.

Currently, as governments across the world attempt to halt the spread of COVID-19, many of their efforts are being hampered by shortages of crucial testing kits. Even in many developed countries, the health system has come to the point of standstill due to the increasing demand for intensive care units. One study reveals that 40,000 women die in a year from breast cancer, which is one woman every 13 minutes dying from this disease every day. Breast cancer remains the number one form of cancer that women are diagnosed with around the world. Any hospital in the world will have a multidisciplinary team of experts including radiologists, oncologists, surgeons, and other respiratory physicians and consultants. This multidisciplinary team uses a wide variety of data taken from CT scans, x-rays, PET scans, biopsies, clinical examination, imaging mammography, ultrasound (sonography), pathology biopsy, and fine-needle aspiration to identify whether a patient has cancer. Individuals with cancer, particularly those who are receiving systemic anticancer treatments, have been postulated to be at increased risk of mortality from COVID-19. Therefore, it is the need of an hour to implement an early detection of cancer that will prevent patients from COVID-19 and due treatment will be provided. Breast cancer that is detected early is far easier to cure, but at the same time it is difficult to detect. An accurate breast cancer staging is an essential task performed by pathologists (experts who examine the laboratory samples) to inform clinical management for the appropriate treatment. For instance, evaluation of the extent of cancer spread by histopathologic analysis or microscopic examination is an important part of breast cancer staging, and the process involves the decision-making of human behavior with time-consuming and costly sample examination and a period of more than one month to have breast cancer type, stage, etc., to decide the appropriate therapy to patients by the time it can affect the results of therapeutic treatments and survival of patients. The application of artificial intelligence (AI) machine learning technology with deep learning algorithms to whole-slide pathology images can potentially improve the diagnostic accuracy of breast cancer at a very early stage. I have developed a simple machine-learning model using the Python programming language and neural network, inspired by the structure of the human brain, with 12 attributes, out of which 10 real-valued features from each cell nucleus obtained from the Wisconsin diagnostic breast cancer dataset available in public domain for the education capacity that explains about the stage of breast cancer M (Malignant) and B (Benign). This model predicts the presence of breast cancer with an accuracy of 98.8% at a very early stage. The continuous development of technology in the medical field will save countless lives and the overall quality of human life will continue to improve over time. Citation Format: Subash Kumar. Early detection of breast cancer during the Covid-19 pandemic using artificial intelligence neural networks [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-003.

Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region

Advances in technology have made it easy to integrate multiple modern systems to form complex system‐of‐systems (SoS) to achieve unparalleled levels of functionality that are otherwise not achievable by the constituent systems in isolation. In fact, with the recent explosion of machine learning techniques to build autonomous systems such as drones and self‐driving cars, there is a pressing need to ensure that they collaboratively and safely operate in an SoS context. However, in general, the characteristic emergent behaviors of complex SoS – that directly impact its operational measures of success or Measures of Effectiveness (MOEs) – is very difficult, if not impossible, to manually explore, anticipate, and arbitrate just from knowledge of its underlying systems. Further, there are multiple scenarios of evolution in such complex SoS, including evolution in the emergent behavior of the SoS. The continuous, continual, and evolving nature of the SoS and constituent system environment's state and possible actions, adds further complexity. In this paper, we present a novel approach that leverages Reinforcement Learning, a machine learning approach, to inculcate adaptable intelligence in constituent systems to adapt their behaviors in tandem with the evolution of emergent behavior at the SoS level. By augmenting the reward mechanism of RL by leveraging SoS‐Constituent System MOE Relationship, that relates and ranks System MOEs vs. SoS MOEs, we inculcate an Intelligent‐Behavior Evolution Agent, with the necessary constraints to learn to maximize the SoS and system‐level MOEs, while adapting itself to the evolution in SoS. We illustrate our approach and demonstrate its feasibility and potential by applying it to a power grid SoS case example. The effectiveness and performance of the approach are quantified.

System of systems (SoS) is an emerging field in the design and development of complex systems that are built from large scale component systems. A SoS has the following attributes: operational and managerial independence of components, a geographic extent that limits control mechanisms to information exchange, an evolutionary nature, and emergent behavior. The subsystems that comprise the SoS often are built by different organizations with conflicting goals, designed under different assumptions and built to different quality standards. These factors impact fault detection, fault isolation, and fault tolerance and can result in systems that cannot easily be debugged, integrated, or maintained. When fault detection and fault tolerance are deficient, the system may behave in a fragile or brittle manner, randomly and repeatedly crashing. Crashes prevent automated diagnosis algorithms from being executed and can prevent manual root cause analysis by erasing system state. Fragility during system integration can prevent achieving schedule milestones and deadlines. Deficient fault detection and fault isolation also impacts end users and system maintainers. (Think <insert name of infamous project here>).From the system architect's point of view, designing a system that can detect all possible fault conditions across all components can be an extremely difficult, if not impossible challenge. Can any system be trusted to diagnose or repair itself when it has been corrupted by faults? How do you prevent local faults from growing into global failures? The end users may have unreasonable expectations about how the system should behave when components within the SoS behave abnormally or fail. They may expect better behavior than the typical PC. The system maintainers may expect a coherent systems view of failures to isolate faulted components and to provide an orderly and safe shutdown or recovery.(Think power grid blackouts, Telecomm failures, etc.)The most beneficial way to achieve fault tolerance is to design in fault detection and fault reporting such that defined boundaries such as subsystems serve as natural firewalls for fault containment. Although partitioning the system into subsystems for fault containment is well known and practiced, the end result as experienced at the time of system integration is rarely a success. COTS middleware, intended to aid distributed design often becomes in effect a step backwards by providing fertile ground for faults and failures that breach fault containment boundaries. (Think <insert name of OS or middleware vendor here>)What can be done to improve this situation? This paper addresses the system architectural partitioning concept of the Coordinated Atomic Actions (CAA). CAA promotes a different manner of organizing software architecture that improves fault containment across potentially faulty components. CAA was first invented by members of Brian Randell's research group at the University of Newcastle at Tyne in the mid 1990's. CAA promotes the concept of the "transaction" which has been traditionally identified with database applications. When you access your bank account via ATM, you are exercising database transactions within your bank's financial SoS. CAA applies transactions to cooperating concurrent distributed processes, which are the basis for most large complex computing systems.

Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research. Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically. Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources. Handwriting characters remain complex since different individuals have different handwriting styles. This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes. The development is based on an artificial neural network, which is a field of study in artificial intelligence. Different techniques and methods are used to develop a Handwriting character recognition system. However, few of them focus on neural networks. The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.

In this paper we use the method of MRF and neural network to solve the problem of parameters estimation in non-rigid 3D movement. Firstly, the method of MRF is used for modeling the local motion correlation of each feature point, and the 3D coordinates of each feature point are obtained. Then the method of neural network is used for clustering the feature points according to their motion situation. When the neural network reaches stabilization, we can get the motion parameters of each feature point. Finally, we correct the neighborhoods of each feature point according to motion parameters. The experimental results show that our algorithm can correctly estimate the non-rigid motion parameters.

ContextIn contrast to traditional software systems that are mostly created from scratch, current software systems are engineered as a junction of systems already in operation. Examples can be found in domains, such as smart cities, crisis and emergency, IoT, big data, industry 4.0, and connected health systems. Most of them can be considered systems-of-systems (SoS), since they refer to alliances of operational and managerial independent software-intensive systems, which are sometimes distributed over different environments. Therefore, SoS software architectures must be dynamic, evolve over time, and support the execution of emergent behaviors to accomplish SoS missions. They must be also designed to enable the connection of heterogeneous systems, making possible their interoperation, communication, coordination, cooperation, and collaboration, most of the times, in a seamless way. Similar challenges have been addressed by using software mediators as architectural entities. However, the application of mediators in SoS has not been properly explored.GoalThis article introduces MediArch, a layered architecture that considers mediators as first-class software entities to be used in the construction of SoS architectures. Our objective is to present evidence about how MediArch’s can support the construction of SoS architectures.MethodsThe following four steps were conducted to define MediArch: (1) identification of mediation requirements to allow SoS properties; (2) establishment and categorization of twelve types of mediators, for enabling capabilities of communication and control of constituent systems interactions and conversion of heterogeneous messages exchanged through a mediation infrastructure; (3) specification of duties, behaviors, assumptions, and guarantees of mediators; and (4) organization of MediArch in three layers, namely, the constituents & consumer systems layer; the communication, conversion, & coordination layer; and the control layer. This architecture was used as the backbone for the software architectures of two SoS in different domains, namely, flood monitoring system-of-systems (FMSoS), and health-care supportive home system-of-systems (HSH-SoS), for providing evidence on how MediArch supports the architecting process of SoS.ResultsMediArch (1) supports the integration of independent constituent systems, (2) provides strategies to manage emergent behaviors, (3) defines different schemes of control authorities, (4) offers elements to support SoS evolution, and (5) promotes the resilience and adaptability of SoS architectures.ConclusionsAlthough MediArch contributes to the establishment of SoS architectures, some challenges related to performance, resource consumption, security, safety, and non-disruptive reconfigurations must still be overcome.

A challenging issue in the architectural design of Systems-of-Systems (SoS) for the Internet-of-Things (IoT) is how to architect an SoS in a way that the required behavior to fulfil the SoS mission will emerge. Indeed, on the one hand, at design-time, most often we do not know which are the concrete IoT systems that will become constituents of the SoS, these being predominantly identified at run-time; on the other hand, the correct architecture depends not only on the constituent systems but also, largely, on the operational environment where the SoS will be deployed. To address this challenge, this paper investigates the notion of self-organization, whose mechanism makes possible that the IoT constituent systems themselves create and maintain a valid architecture enabling the production of the required emergent behavior to fulfil the SoS mission. In particular, it describes how SosADL, a formal SoS Architecture Description Language (ADL), based on the novel π-Calculus for SoS, supports the architectural description of self-organizing SoSs for the IoT, upwardly causing SoS emergent behaviors at run-time.

This article has been retracted by the publisher.

While systems of systems (SoS) are starting to reach the market, it is not entirely evident how to analyze safety, and on a high level also security, of such systems. In fact, specific characteristics of SoS, such as independence, changing constitution, evolutionary development, and emergent behavior, provide certain challenges to the safety analysis. In this paper, such challenges are summarized and a systems theoretic safety analysis method, abbreviated as STAMP, is evaluated on an automotive SoS application example. In conclusion, STAMP seems well positioned to serve as a base for a future method for safety and, to a certain degree, security analysis of SoS, although some work remains to be done. The advantages and limitations of the STAMP approach when dealing with SoS are discussed.

Nowadays there is growing awareness of the importanceof Systems of Systems (SoS) which are large-scalesystems composed of complex systems. SoS possess specificproperties when compared with monolithic complex systems, inparticular: operational independence, managerial independence,evolutionary development, emergent behavior and geographicdistribution. One of the current main challenges is the impact ofthese properties on SoS security modeling and analysis. In thisresearch proposal, we introduce a new method incorporating aprocess, a language and a software architectural tool to model,analyze and predict security architectural alternatives of SoS.Thus security will be taken into account as soon as possible inthe life cycle of the SoS, making it less expensive

This paper describes research into the applicability and value of architecture modeling techniques in conducting failure analysis on a system that is currently in service. Many Systems of Systems (SoS) currently in service did not include architecture modeling in their design and integration phases. In the absence of architectural models, knowledge of critical items, interfaces, and interactions, is difficult to learn, communicate, and maintain. Failure analysis, particularly for SoS, requires comprehensive system knowledge to produce accurate and timely results. This paper contributes specifically by summarizing existing model-based failure analysis research in terms of applicability to in-service systems and by focusing on Fault Tree Analysis (FTA) and Failure Modes Effects Analysis (FMEA). Much of the existing research in this area is either specific to design or development phases of system lifecycle, or presupposes that architectural models exist, and does not approach the topic from the perspective of value and practicality to an in-service system. Firstly, a general analysis of the commonality of principles, methods, and resources used for failure analysis with those of system architecture modeling is presented. The second contribution is a review of system and SoS modeling techniques and current research as applied to these failure analysis methods. The paper concludes with a summary of model applicability to FTA and FMEA, and a brief analysis of the studied methods, which will guide future research and evaluation of architecture modeling-based methods for failure analysis.

A System-of-Systems (SoS) is evolutionary developed from independent systems to achieve missions not possible to be accomplished by a single system alone. They are architecturally designed to exhibit emergent behavior, i.e. a new behavior that stem from the interactions among constituent systems, but cannot be deduced from the behaviors of the constituent systems themselves. This paper presents the concepts and constructs of a novel Architecture Description Language (ADL), named SosADL, for architecturally describing emergent behaviors of software-intensive SoSs. It refines the notion of emergent behavior and details how SosADL enables the architectural description of emergent behaviors. In particular, it demonstrates SosADL features for emergent behavior through an excerpt of a real application for architecting a Reconnaissance SoS, focusing on the flocking behavior of a fleet of Unmanned Aerial Vehicles (UAVs).