Computer Science

Metrics can be used by businesses to make more objective decisions based on data. Software startups in particular are characterized by the uncertain or even chaotic nature of the contexts in which they operate. Using data in the form of metrics can help software startups to make the right decisions amidst uncertainty and limited resources. However, whereas conventional business metrics and software metrics have been studied in the past, metrics in the spe-cific context of software startup are not widely covered within academic literature. To promote research in this area and to create a starting point for it, we have conducted a multi-vocal literature review focusing on practitioner literature in order to compile a list of metrics used by software startups. Said list is intended to serve as a basis for further research in the area, as the metrics in it are based on suggestions made by practitioners and not empirically verified.

The dialogue develops arguments for and against adopting a new world system, info-computationalist naturalism, that is poised to replace the traditional mechanistic world system. We try to figure out what the info-computational paradigm would mean, in particular its pancomputationalism. We make some steps towards developing the notion of computing that is necessary here, especially in relation to traditional notions. We investigate whether pancomputationalism can possibly provide the basic causal structure to the world, whether the overall research programme appears productive and whether it can revigorate computationalism in the philosophy of mind.

The number of research papers written has been growing at least linearly -- if not exponentially -- in recent years. In proportion, the amount of time a reader allocates per paper has been decreasing. While an accessible paper will be appreciated by a large audience, hard-to-read papers may remain obscure for a long time regardless of scientific merit. Unfortunately, there is still insufficient emphasis on good written and oral communication skills in technical disciplines, especially in engineering. As an academic, I have realised over the years that I keep telling my students the same things over and over again when they write papers, reports, presentations, and theses. This article contains some of those suggestions and serves as a limited template for organising research articles. I have adopted a very practical and personal approach and don't claim that this is a formal contribution to the scientific communication literature. However, I hope that this article will not only make my life a bit easier but also help other graduate students and academic supervisors.

The article presents the main milestones of the science and technology biography of Ivan Edward Sutherland. The influence of the family and the school on the development of its research competencies is shown, and little-known biographical facts explaining the evolution of his scientific interests is presented: from dynamic object-oriented graphic systems through systems of virtual reality to asynchronous circuits.

Alan Turing's pioneering work on computability, and his ideas on morphological computing support Andrew Hodges' view of Turing as a natural philosopher. Turing's natural philosophy differs importantly from Galileo's view that the book of nature is written in the language of mathematics (The Assayer, 1623). Computing is more than a language of nature as computation produces real time physical behaviors. This article presents the framework of Natural Info-computationalism as a contemporary natural philosophy that builds on the legacy of Turing's computationalism. Info-computationalism is a synthesis of Informational Structural Realism (the view that nature is a web of informational structures) and Natural Computationalism (the view that nature physically computes its own time development). It presents a framework for the development of a unified approach to nature, with common interpretation of inanimate nature as well as living organisms and their social networks. Computing is understood as information processing that drives all the changes on different levels of organization of information and can be modeled as morphological computing on data sets pertinent to informational structures. The use of infocomputational conceptualizations, models and tools makes possible for the first time in history the study of complex selforganizing adaptive systems, including basic characteristics and functions of living systems, intelligence, and cognition.

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

Relative to digital computation, analog computation has been neglected in the philosophical literature. To the extent that attention has been paid to analog computation, it has been misunderstood. The received view -- that analog computation has to do essentially with continuity -- is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analog computers. Instead of the received view, I develop an account of analog computation in terms of a particular type of analog representation that allows for discontinuity. This account thus characterizes all types of analog computation, whether continuous or discrete. Furthermore, the structure of this account can be generalized to other types of computation: analog computation essentially involves analog representation, whereas digital computation essentially involves digital representation. Besides being a necessary component of a complete philosophical understanding of computation in general, understanding analog computation is important for computational explanation in contemporary neuroscience and cognitive science.

Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us about the progress of science. Furthermore, it is emphasized the important role played by that nonlinear dynamical systems for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.

In this paper, we analyze axiomatic issues of unconventional computations from a methodological and philosophical point of view. We explain how the new models of algorithms changed the algorithmic universe, making it open and allowing increased flexibility and creativity. However, the greater power of new types of algorithms also brought the greater complexity of the algorithmic universe, demanding new tools for its study. That is why we analyze new powerful tools brought forth by the axiomatic theory of algorithms, automata and computation.

We argue that the boldest claims of Big Data are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of Big Data are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, nonlocality and hyperdimensions which one encounters frequently in multiscale modelling.