952 research outputs found

    DEVELOPING COMPETENCE FOR INNOVATION IN KNOWLEDGE PRACTICE: an exploration of the sustainability science-policy interface

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    This thesis is motivated by institutional claims for a “new type of knowledge” in the sustainability science-policy interface. It thus explores the thinking and practice of experts in the field about professional competencies necessary to induce required innovations in their knowledge practice. The thesis proposes a novel conceptual framework, synthesising (1) five key features informing claimed innovations in knowledge practice of science-policy sustainability ‘boundary organisations’, (2) a set of ten differentiating individual competencies deemed critical to induce such type of innovations and (3) required approaches to effective development of such competencies. In doing so, this thesis suggests an operative framework to engage with a competence-based approach in response to the need for innovations in knowledge practice within boundary organisations. Under the conceptual framework above, the thesis engages in an empirical work exploring the thinking and praxis of experts in the field, around three key questions: (1) How do practitioners in the field perceive the need for and the pertinence of such type of innovations, (2) How do they relate to the notion of individual competence and the need for different types of competencies to induce innovations in their own knowledge practice and (3) How can professionals working in the science-policy interface most effectively learn and develop such new set of competences, given their specific organisational / institutional contexts? Methodologically, this thesis engaged a combined set of empirical research instruments, mostly including semi-structured interviews with professionals operating in the sustainability sciencepolicy interface, three focus-groups in The Netherlands, Portugal and the UK, with actors operating within the remit of sustainability boundary organizations, and participatory observation within the European Environment Agency. Outcomes of this research indicate that, while the need for a new type of knowledge is clearly acknowledged by practitioners in ‘boundary organisations’, notions associated with required innovations in knowledge practice – such as co-creation, systems thinking, transdisciplinarity, reflexivity and action-orientated knowledge – are still subject to ambiguity and controversy within the institutional context they operate. As practitioners struggle to engage the notion of individual competence in this debate, the type of competencies deemed critical to induce required innovations in their knowledge practice resonates with their own experience. Experts in boundary organisations identify though a lack of institutional frameworks to support their efforts to generate innovations in knowledge practice. While this research synthetises and presents existing examples of learning programmes and approaches to help develop such type of competencies, practitioners in the field manifest scepticism on the extent to which such learning approaches are feasible in their given institutional settings

    1st Design Factory Global Network Research Conference ‘Designing the Future’ 5-6 October 2022

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    DFGN.R 2022 -Designing the Future - is the first research conference organised by the Design Factory Global Network. The open event offers the opportunity for all like-minded educators, designers and researchers to share their insights and inspire others on education, methods, practices and ecosystems of co-creation and innovation. The DFGN.R conference is a two-day event hosted on-site in Leeuwarden, the Netherlands. The conference is organized alongside International Design Factory Week 2022, the annual gathering of DFGN members. This year's conference is organized in collaboration with Aalto University from Helsinki Finland and hosted by the NHL Stenden University of Applied Sciences

    Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

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    Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches O(106)\mathbf{O}(10^6) servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in ≈500ps\approx 500 ps and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where 3×3\times less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in O(10−3)s\mathbf{O}(10^{-3}) s. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved >20%>20\% with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

    ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

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    Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

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    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

    Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

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    Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

    3D Visualisation - An Application and Assessment for Computer Network Traffic Analysis

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    The intent of this research is to develop and assess the application of 3D data visualisation to the field of computer security. The growth of available data relating to computer networks necessitates a more efficient and effective way of presenting information to analysts in support of decision making and situational awareness. Advances in computer hardware and display software have made more complex and interactive presentation of data in 3D possible. While many attempts at creation of data-rich 3D displays have been made in the field of computer security, they have not become the tool of choice in the industry. There is also a limited amount of published research in the assessment of these tools in comparison to 2D graphical and tabular approaches to displaying the same data. This research was conducted through creation of a novel abstraction framework for visualisation of computer network data, the Visual Interactive Network Analysis Framework (VINAF). This framework was implemented in software and the software prototype was assessed using both a procedural approach applied to a published forensics challenge and also through a human participant based experiment. The key contributions to the fields of computer security and data visualisation made by this research include the creation of a novel abstraction framework for computer network traffic which features several new visualisation approaches. An implementation of this software was developed for the specific cybersecurity related task of computer network traffic analysis and published under an open source license to the cybersecurity community. The research contributes a novel approach to human-based experimentation developed during the COVID-19 pandemic and also implemented a novel procedure-based testing approach to the assessment of the prototype data visualisation tool. Results of the research showed, through procedural experimentation, that the abstraction framework is effective for network forensics tasks and exhibited several advantages when compared to alternate approaches. The user participation experiment indicated that most of the participants deemed the abstraction framework to be effective in several task related to computer network traffic analysis. There was not a strong indication that it would be preferred over existing approaches utilised by the participants, however, it would likely be used to augment existing methods

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    Towards a Peaceful Development of Cyberspace - Challenges and Technical Measures for the De-escalation of State-led Cyberconflicts and Arms Control of Cyberweapons

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    Cyberspace, already a few decades old, has become a matter of course for most of us, part of our everyday life. At the same time, this space and the global infrastructure behind it are essential for our civilizations, the economy and administration, and thus an essential expression and lifeline of a globalized world. However, these developments also create vulnerabilities and thus, cyberspace is increasingly developing into an intelligence and military operational area – for the defense and security of states but also as a component of offensive military planning, visible in the creation of military cyber-departments and the integration of cyberspace into states' security and defense strategies. In order to contain and regulate the conflict and escalation potential of technology used by military forces, over the last decades, a complex tool set of transparency, de-escalation and arms control measures has been developed and proof-tested. Unfortunately, many of these established measures do not work for cyberspace due to its specific technical characteristics. Even more, the concept of what constitutes a weapon – an essential requirement for regulation – starts to blur for this domain. Against this background, this thesis aims to answer how measures for the de-escalation of state-led conflicts in cyberspace and arms control of cyberweapons can be developed. In order to answer this question, the dissertation takes a specifically technical perspective on these problems and the underlying political challenges of state behavior and international humanitarian law in cyberspace to identify starting points for technical measures of transparency, arms control and verification. Based on this approach of adopting already existing technical measures from other fields of computer science, the thesis will provide proof of concepts approaches for some mentioned challenges like a classification system for cyberweapons that is based on technical measurable features, an approach for the mutual reduction of vulnerability stockpiles and an approach to plausibly assure the non-involvement in a cyberconflict as a measure for de-escalation. All these initial approaches and the questions of how and by which measures arms control and conflict reduction can work for cyberspace are still quite new and subject to not too many debates. Indeed, the approach of deliberately self-restricting the capabilities of technology in order to serve a bigger goal, like the reduction of its destructive usage, is yet not very common for the engineering thinking of computer science. Therefore, this dissertation also aims to provide some impulses regarding the responsibility and creative options of computer science with a view to the peaceful development and use of cyberspace
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