625 research outputs found
Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability
Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far.
In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs.
We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design â one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases â one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes.
We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research
Writing Facts: Interdisciplinary Discussions of a Key Concept in Modernity
"Fact" is one of the most crucial inventions of modern times. Susanne Knaller discusses the functions of this powerful notion in the arts and the sciences, its impact on aesthetic models and systems of knowledge. The practice of writing provides an effective procedure to realize and to understand facts. This concerns preparatory procedures, formal choices, models of argumentation, and narrative patterns. By considering "writing facts" and "writing facts", the volume shows why and how "facts" are a result of knowledge, rules, and norms as well as of description, argumentation, and narration. This approach allows new perspectives on »fact« and its impact on modernity
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
Enabling supernova cosmology with large time-domain surveys
It was recently discovered that the expansion of the Universe is accelerating. Type Ia supernovae (SN Ia) were crucial for this discovery and to constrain cosmological parameters. Current and upcoming large time-domain surveys will revolutionise the field by discovering at least one order of magnitude more SNe than the currently available datasets, which will lead to tighter cosmological parameter constraints. However, these surveys will also bring challenges due to the volume of data they observe. Thus, we require new methods to analyse, understand, and extract cosmological constraints from the data. In particular, the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) must rely on photometric classification to identify the observed SNe, instead of the traditional spectroscopic confirmation. In this thesis, we develop a methodology to perform this photometric classification based on dataset augmentation, wavelet features, and a machine learning classifier. Specifically, we find that augmenting the training set to make its features similar to the dataset we want to classify is crucial. Next, we use our methodology to measure the impact of the LSST observing strategy in photometric classification; this work contributes towards the community-focused optimisation of the observing strategy. Since the above work used simulated data, we next set a benchmark for the classification performance of our approach using the Zwicky Transient Facility real data; this is a precursor survey to LSST. Finally, we present a proof-of-concept of a neural network to predict lensed SN Ia parameters from light curves and images of those events
The mad manifesto
The âmad manifestoâ project is a multidisciplinary mediated investigation into the circumstances by which mad (mentally ill, neurodivergent) or disabled (disclosed, undisclosed) students faced far more precarious circumstances with inadequate support models while attending North American universities during the pandemic teaching era (2020-2023).
Using a combination of âemergency remote teachingâ archival materials such as national student datasets, universal design for learning (UDL) training models, digital classroom teaching experiments, university budgetary releases, educational technology coursewares, and lived experience expertise, this dissertation carefully retells the story of âaccessibilityâ as it transpired in disabling classroom containers trapped within intentionally underprepared crisis superstructures. Using rhetorical models derived from critical disability studies, mad studies, social work practice, and health humanities, it then suggests radically collaborative UDL teaching practices that may better pre-empt the dynamic needs of dis/abled students whose needs remain direly underserviced.
The manifesto leaves the reader with discrete calls to action that foster more critical performances of intersectionally inclusive UDL classrooms for North American mad students, which it calls âmad-positiveâ facilitation techniques:
1. Seek to untie the bond that regards the digital divide and access as synonyms.
2. UDL practice requires an environment shift that prioritizes change potential.
3. Advocate against the usage of UDL as a for-all keystone of accessibility.
4. Refuse or reduce the use of technologies whose primary mandate is dataveillance.
5. Remind students and allies that university space is a non-neutral affective container.
6. Operationalize the tracking of student suicides on your home campus.
7. Seek out physical & affectual ways that your campus is harming social capital potential.
8. Revise policies and practices that are ability-adjacent imaginings of access.
9. Eliminate sanist and neuroscientific languaging from how you speak about students.
10. Vigilantly interrogate how ânormalâ and âbelongâ are socially constructed.
11. Treat lived experience expertise as a gift, not a resource to mine and to spend.
12. Create non-psychiatric routes of receiving accommodation requests in your classroom.
13. Seek out uncomfortable stories of mad exclusion and consider carceral logicâs role in it.
14. Center madness in inclusive methodologies designed to explicitly resist carceral logics.
15. Create counteraffectual classrooms that anticipate and interrupt kairotic spatial power.
16. Strive to refuse comfort and immediate intelligibility as mandatory classroom presences.
17. Create pathways that empower cozy space understandings of classroom practice.
18. Vector students wherever possible as dynamic ability constellations in assessment
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding
Artificial intelligenceâs (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AIâs conceptual and promissory settings tell about the current rebranding of AI?
The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AIâs contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AIâs shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise
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