Modern software cybernetics: new trends

Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research

Modern software cybernetics: New trends

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research

Using component ensembles for modeling autonomic component collaboration in smart farming

Smart systems have become key solutions for many application areas including autonomous farming. The trend we can see now in the smart systems is that they shift from single isolated autonomic and self-adaptive components to larger ecosystems of heavily cooperating components. This increases the reliability and often the cost-effectiveness of the system by replacing one big costly device with a number of smaller and cheaper ones. In this paper, we demonstrate the effect of synergistic collaboration among autonomic components in the domain of smart farming---in particular, the use-case we employ in the demonstration stems from the AFarCloud EU project. We exploit the concept of autonomic component ensembles to describe situation-dependent collaboration groups (so called ensembles). The paper shows how the autonomic component ensembles can easily capture complex collaboration rules and how they can include both controllable autonomic components (i.e. drones) and non-controllable environment agents (flocks of birds in our case). As part of the demonstration, we provide an open-source implementation that covers both the specification of the autonomic components and ensembles of the use case, and the discrete event simulation and real-time visualization of the use case. We believe this is useful not only to demonstrate the effectiveness of architectures of collaborative autonomic components for dealing with real-life tasks, but also to build further experiments in the domain.This is the authors' version of the paper: P. Hnětynka, T. Bureš, I. Gerostathopoulos, J. Pacovský: Using Component Ensembles for Modeling Autonomic Component Collaboration in Smart Farming, in Proceedings of SEAMS 2020, Seoul, Korea, 2020. The final published version can be found at https://doi.org/10.1145/3387939.339159

Proceedings of the 4th Workshop of the MPM4CPS COST Action

Proceedings of the 4th Workshop of the MPM4CPS COST Action with the presentations delivered during the workshop and papers with extended versions of some of them

A conceptual framework for uncertainty in software systems and its application to software architectures

The development and operation of a software system involve many aspects including processes, artefacts, infrastructure and environments. Most of these aspects are vulnerable to uncertainty. Thus, the identification, representation and management of uncertainty in software systems is important and will be of interest to many stakeholders in software systems. The hypothesis of this work is that such consideration would benefit from an underlying conceptual framework that allows stakeholders to characterise, analyse and mitigate uncertainties. This PhD proposes a framework to provide a generic foundation for the systematic and explicit consideration of uncertainty in software systems by consolidating and extending existing approaches to dealing with uncertainty, which are typically tailored to specific domains or artefacts. The thesis applies the framework to software architectures, which are fundamental in determining the structure, behaviour and qualities of software systems and are thus suited to serve as an exemplar artefact. The framework is evaluated using the software architectures of case studies from 3 different domains. The contributions of the research to the study of uncertainty in software systems include a literature review of approaches to managing uncertainty in software architecture, a review of existing work on uncertainty frameworks related to software systems, a conceptual framework for uncertainty in software systems, a conceptualisation of the workbench infrastructure as a basis for building an uncertainty consideration workbench of tools for representing uncertainty as part of software architecture descriptions, and an evaluation of the uncertainty framework using three software architecture case studies

Research and Creative Activity, July 1, 2020-June 30, 2021: Major Sponsored Programs and Faculty Accomplishments in Research and Creative Activity, University of Nebraska-Lincoln

Foreword by Bob Wilhelm, Vice Chancellor for Research and Economic Development, University of Nebraska-Lincoln: This booklet highlights successes in research, scholarship and creative activity by University of Nebraska–Lincoln faculty during the fiscal year running July 1, 2020, to June 30, 2021. It lists investigators, project titles and funding sources on major grants and sponsored awards received during the year; fellowships and other recognitions and honors bestowed on our faculty; books and chapters published by faculty; performances, exhibitions and other examples of creative activity; patents and licensing agreements issued; National Science Foundation I-CORPS teams; and peer-reviewed journal articles and conference presentations. In recognition of the important role faculty have in the undergraduate experience at Nebraska, this booklet notes the students and mentors participating in the Undergraduate Creative Activities and Research Experience (UCARE) and the First-Year Research Experience (FYRE) programs. While metrics cannot convey the full impact of our work, they are tangible measures of growth. A few achievements of note: • UNL achieved a record $320 million in total research expenditures in FY 2020, a 43• Our faculty earned 1,508 sponsored research awards in FY 2020. University-sponsored industry activity also spurred economic growth for Nebraska. • Nebraska Innovation Campus created 1,948 jobs statewide and had a total economic impact of$372 million. • Industry sponsorship supported $19.2 million in research expenditures. • NUtech Ventures brought in$6.48 million in licensing income. I applaud the Nebraska Research community for its determination and commitment during a challenging year. Your hard work has made it possible for our momentum to continue growing. Our university is poised for even greater success. The Grand Challenges initiative provides a framework for developing bold ideas to solve society’s greatest issues, which is how we will have the greatest impact as an institution. Please visit research.unl.edu/grandchallenges to learn more. We’re also renewing our campus commitment to a journey of anti-racism and racial equity, which is among the most important work we’ll do. I am pleased to present this record of accomplishments. Contents Awards of $5 Million or More Awards of$1 Million to $4,999,999 Awards of$250,000 to $999,999 50 Early Career Awards Arts and Humanities Awards of$250,000 or More Arts and Humanities Awards of $50,000 to$249,999 Arts and Humanities Awards of $5,000 to$49,999 Patents License Agreements National Science Foundation Innovation Corps Teams Creative Activity Books Recognitions and Honors Journal Articles 105 Conference Presentations UCARE and FYRE Projects Glossar

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas