Toward Open Science at the European Scale: Geospatial Semantic Array Programming for Integrated Environmental Modelling

[Excerpt] Interfacing science and policy raises challenging issues when large spatial-scale (regional, continental, global) environmental problems need transdisciplinary integration within a context of modelling complexity and multiple sources of uncertainty. This is characteristic of science-based support for environmental policy at European scale, and key aspects have also long been investigated by European Commission transnational research. Approaches (either of computational science or of policy-making) suitable at a given domain-specific scale may not be appropriate for wide-scale transdisciplinary modelling for environment (WSTMe) and corresponding policy-making. In WSTMe, the characteristic heterogeneity of available spatial information and complexity of the required data-transformation modelling (D-TM) appeal for a paradigm shift in how computational science supports such peculiarly extensive integration processes. In particular, emerging wide-scale integration requirements of typical currently available domain-specific modelling strategies may include increased robustness and scalability along with enhanced transparency and reproducibility. This challenging shift toward open data and reproducible research (open science) is also strongly suggested by the potential - sometimes neglected - huge impact of cascading effects of errors within the impressively growing interconnection among domain-specific computational models and frameworks. Concise array-based mathematical formulation and implementation (with array programming tools) have proved helpful in supporting and mitigating the complexity of WSTMe when complemented with generalized modularization and terse array-oriented semantic constraints. This defines the paradigm of Semantic Array Programming (SemAP) where semantic transparency also implies free software use (although black-boxes - e.g. legacy code - might easily be semantically interfaced). A new approach for WSTMe has emerged by formalizing unorganized best practices and experience-driven informal patterns. The approach introduces a lightweight (non-intrusive) integration of SemAP and geospatial tools - called Geospatial Semantic Array Programming (GeoSemAP). GeoSemAP exploits the joint semantics provided by SemAP and geospatial tools to split a complex D-TM into logical blocks which are easier to check by means of mathematical array-based and geospatial constraints. Those constraints take the form of precondition, invariant and postcondition semantic checks. This way, even complex WSTMe may be described as the composition of simpler GeoSemAP blocks. GeoSemAP allows intermediate data and information layers to be more easily and formally semantically described so as to increase fault-tolerance, transparency and reproducibility of WSTMe. This might also help to better communicate part of the policy-relevant knowledge, often diffcult to transfer from technical WSTMe to the science-policy interface. [...

The Topological Field Theory of Data: a program towards a novel strategy for data mining through data language

This paper aims to challenge the current thinking in IT for the 'Big Data' question, proposing - almost verbatim, with no formulas - a program aiming to construct an innovative methodology to perform data analytics in a way that returns an automaton as a recognizer of the data language: a Field Theory of Data. We suggest to build, directly out of probing data space, a theoretical framework enabling us to extract the manifold hidden relations (patterns) that exist among data, as correlations depending on the semantics generated by the mining context. The program, that is grounded in the recent innovative ways of integrating data into a topological setting, proposes the realization of a Topological Field Theory of Data, transferring and generalizing to the space of data notions inspired by physical (topological) field theories and harnesses the theory of formal languages to define the potential semantics necessary to understand the emerging patterns

Teaching Computational Thinking

Computational thinking is a fundamental skill for everyone, not just computer scientists. Computational thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information processing agent. Teaching Computational Thinking introduces the fundamental principles of communicating computing to learners across all levels. The book delves into the philosophical and psychological foundations of computer science as a school subject as well as specific teaching methods, curriculum, tools, and research approaches in computing education. This book is intended as a guide and teaching companion for pre-service and in-service computer science teachers

Teaching Natural Computation

This paper consists of a discussion of the potential impact on computer science education of regarding computation as a property of the natural world, rather than just a property of artifacts specifically created for the purpose of computing. Such a perspective is becoming increasingly important: new computing paradigms based on the natural computational properties of the world are being created, scientific questions are being answered using computational ideas, and philosophical debates on the nature of computation are being formed. This paper discusses how computing education might react to these developments, goes on to discuss how these ideas can help to define computer science as a discipline, and reflects on our experience at Kent in teaching these subjects

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Abstraction Fashion: Seeing and Making Network Abstractions and Computational Fashions

Human life today is enmeshed with network organisms. What we value, the ways we talk, and the subject matter we pay attention to are all dependent on and depended upon by the networks that dominate our imagination. The internet, private social platforms, and the virtual and physical supply chains that create the hardware, software, and memetic abstractions with which we think are all examples of network organisms. Each has found a viability mechanism that permits it to survive and thrive in the present moment. Each viability mechanism creates its own unique incentives for self-perpetuation, which drive the outward appearances with which we are familiar. These incentives manifest as product forms, interface abstractions, and socially optimized beliefs and identities. To grapple with what drives the abstractions these network organisms output, this dissertation builds a worldview for seeing and making with computational networks. Computing machines are composed of abstractions, simulate abstractions, and project their abstractions onto the world. Creating in this medium requires resources that can be acquired through attention manipulation and fashion performance. The text culminates in an appendix documenting ewaste club, an art research-creation project that combines wearable cameras, supply chain inspired fashion, and disposable computers. Through a mixture of practical projects, historical analysis, and technical explanation, this dissertation proposes several new concepts linking fashion, the arts, and computation to making in the time of networks

Finding high-quality grey literature for use as evidence in software engineering research.

Background: Software engineering research often uses practitioners as a source of evidence in their studies. This evidence is usually gathered through empirical methods such as surveys, interviews and ethnographic research. The web has brought with it the emergence of the social programmer. Software practitioners are publishing their opinions online through blog articles, discussion boards and Q&A sites. Mining these online sources of information could provide a new source of evidence which complements traditional evidence sources. There are benefits to the adoption of grey literature in software engineering research (such as bridging the gap between the state–of–art where research typically operates and the state–of–practice), but also significant challenges. The main challenge is finding grey literature which is of high– quality to the researcher given the vast volume of grey literature available on the web. The thesis defines the quality of grey literature in terms of its relevance to the research being undertaken and its credibility. The thesis also focuses on a particular type of grey literature that has been written by soft- ware practitioners. A typical example of such grey literature is blog articles, which are specifically used as examples throughout the thesis. Objectives: There are two main objectives to the thesis; to investigate the problems of finding high–quality grey literature, and to make progress in addressing those problems. In working towards these objectives, we investigate our main research question, how can researchers more effectively and efficiently search for and then select the higher–quality blog–like content relevant to their research? We divide this question into twelve sub–questions, and more formally define what we mean by ‘blog–like content.’ Method: To achieve the objectives, we first investigate how software engineering researchers define and assess quality when working with grey literature; and then work towards a methodology and also a tool–suite which can semi–automate the identification and the quality assessment of relevant grey literature for use as evidence in the researchers study. To investigate how software engineering researchers define and assess quality, we first conduct a literature review of credibility assessment to gather a set of credibility criteria. We then validate those criteria through a survey of software engineering researchers. This gives us an overall model of credibility assessment within software engineering research. We next investigate the empirical challenges of measuring quality and develop a methodology which has been adapted from the case survey methodology and aims to address the problems and challenges identified. Along with the methodology is a suggested tool–suite which is intended to help researchers in automating the application of a subset of the credibility model. The tool–suite developed supports the methodology by, for example, automating tasks in order to scale the analysis. The use of the methodology and tool–suite is then demonstrated through three examples. These examples include a partial evaluation of the methodology and tool–suite. Results: Our literature review of credibility assessment identified a set of criteria that have been used in previous research. However, we also found a lack of definitions for both the criteria and, more generally, the term credibility. Credibility assessment is a difficult and subjective task that is particular to each individual. Research has addressed this subjectivity by conducting studies that look at how particular user groups assess credibility e.g. pensioners, university students, the visually impaired, however none of the studies reviewed software engineering researchers. Informed by the literature review, we conducted a survey which we believe is the first study on the credibility assessment of software engineering researchers. The results of the survey are a more refined set of criteria, but also a set that many (approximately 60%) of the survey participants believed generalise to other types of media (both practitioner–generated and researcher–generated). We found that there are significant challenges in using blog–like content as evidence in research. For example, there are the challenges of identifying the high–quality content from the vast quantity available on the web, and then creating methods of analysis which are scalable to handle that vast quantity. In addressing these challenges, we produce: a set of heuristics which can help in finding higher–quality results when searching using traditional search engines, a validated list of reasoning markers that can aid in assessing the amount of reasoning within a document, a review of the current state of the experience mining domain, and a modifiable classification schema for classifying the source of URLs. With credibility assessment being such a subjective task, there can be no one–size–fits–all method to automating quality assessment. Instead, our methodology is intended to be used as a framework in which the researcher using it can swap out and adapt the criteria that we assess for their own criteria based on the context of the study being undertaken and the personal preference of the researcher. We find from the survey that there are a variety of attitude’s towards using grey literature in software engineering research and not all respondents view the use of grey literature as evidence in the way that we do (i.e. as having the same benefits and threats as other traditional methods of evidence gathering). Conclusion: The work presented in this thesis makes significant progress towards answering our research question and the thesis provides a foundation for future research on automated quality assessment and credibility. Adoption of the tools and methodology presented in this thesis can help more effectively and efficiently search for and select higher–quality blog–like content, but there is a need for more substantial research on the credibility assessment of software engineering researchers, and a more extensive credibility model to be produced. This can be achieved through replicating the literature review systematically, accepting more studies for analysis, and by conducting a more extensive survey with a greater number, and more representative selection, of survey respondents. With a more robust credibility model, we can have more confidence in the criteria that we choose to include within the methodology and tools, as well as automating the assessment of more criteria. Throughout the re- search, there has been a challenge in aggregating the results after assessing each criterion. Future research should look towards the adoption of machine learning methods to aid with this aggregation. We believe that the criteria and measures used by our tools can serve as features to machine learning classifiers which will be able to more accurately assess quality. However, be- fore such work is to take place, there is a need for annotated data–sets to be developed

De-Gendering informatischer Artefakte: Grundlagen einer kritisch-feministischen Technikgestaltung

Gender studies in computer science was only recently established at German universities. This research area is often understood as either addressing the problem of getting more women into IT professions or focussing on alleged gender differences in the design and use of IT. In contrast, the main objective of this dissertation is to identify and systemize gendering processes in products, theories, methods and assumptions of computer science (i.e. computational artifacts), in order to propose technology design methods, which aim at de-gendering these artefacts. The thesis focuses on three topics of inquiry: 1. Theoretical foundation: How can gendering and de-gendering processes of computational artifacts be theorized? 2. Practices of gendering: What are dimensions and mechanisms of gendering computational computational artifacts? 3. Methodological concepts for de-gendering: How can computational artefacts be designed, which can be characterized as de-gendered technologie