8,647 research outputs found
Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.
Mobile heritage apps have become one of the most popular means for audience
engagement and curation of museum collections and heritage contexts. This
raises practical and ethical questions for both researchers and practitioners, such
as: what kind of audience engagement can be built using mobile apps? what are
the current approaches? how can audience engagement with these experience
be evaluated? how can those experiences be made more resilient, and in turn
sustainable? In this thesis I explore experience design scholarships together with
personal professional insights to analyse digital heritage practices with a view to
accelerating thinking about and critique of mobile apps in particular. As a result,
the chapters that follow here look at the evolution of digital heritage practices,
examining the cultural, societal, and technological contexts in which mobile
heritage apps are developed by the creative media industry, the academic
institutions, and how these forces are shaping the user experience design
methods. Drawing from studies in digital (critical) heritage, Human-Computer
Interaction (HCI), and design thinking, this thesis provides a critical analysis of
the development and use of mobile practices for the heritage. Furthermore,
through an empirical and embedded approach to research, the thesis also
presents auto-ethnographic case studies in order to show evidence that mobile
experiences conceptualised by more organic design approaches, can result in
more resilient and sustainable heritage practices. By doing so, this thesis
encourages a renewed understanding of the pivotal role of these practices in the
broader sociocultural, political and environmental changes.AHRC REAC
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical
Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just
at the economic level but also in terms of physical damage and even loss of human life. Complementing
traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring
Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are
in place and compliant with standards and internal policies. Forensics assist the investigation of past security
incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can
be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the
latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing
in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of
tackling the requirements imposed by massively distributed and complex Industrial Automation and Control
Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and
redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced
a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the
collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic
template for a converged platform. These results are intended to guide future research on forensics and
compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio
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Towards a systems-based framework for understanding the diffusion of technology: A case study of a modest technological innovation in the multi-agency context of policing
Technological innovation in policing is being given greater emphasis. In public discourse about technology and policing, there is often a focus on large-scale projects that are known to fail, sometimes at significant cost. The implementation of smaller innovations are often overlooked. This thesis examines practice of innovation and adoption in the context of multi-agency working.
The literature review in this thesis reveals that little is known about contexts where decision making does not rest with the police and exposes potential limitations in the use of diffusion and adoption frameworks/models. The research question is: In the context of multi-agency diffusion and adoption of a technology to enhance policing, can systems thinking techniques enhance, or even replace, existing frameworks and models?
This empirical research study looks at the adoption of a relatively simple technology that scans identification documents. However, the decision to adopt and implement an ID scanner takes place within a complex setting. Tracking an adoption decision requires understanding of the various actors and their roles. The research includes 48 semi-structured interviews with police officers, premises owners and managers and other stakeholders involved in the decision to adopt an ID scanner. Their perceptions of the history leading to an adoption decision, their own role and that of other key actors is examined.
Initial analysis takes place using spray diagrams and further analysis is made through the lenses of existing diffusion and adoption frameworks/models. Subsequently systems thinking techniques are deployed and the additional insights they provide are highlighted. This research finds that systems thinking can extend understanding of multi-agency diffusion and adoption decisions when compared with solely utilising existing frameworks/models. Finally, the research proposes a systems-based framework for collaborative diffusion and adoption analysis
A clinical decision support system for detecting and mitigating potentially inappropriate medications
Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly.
Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects.
Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches.
Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice
AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0
The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems
Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP
Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP
Towards rapid modeling and prototyping of indoor and outdoor monitoring applications
Nowadays, the capability to remotely monitor indoor and outdoor environments would allow to reduce energy consumption and improve the overall management and usersâ experience of network application systems. The most known solutions adopting remote control are related to domotics (e.g., smart homes and industry 4.0 applications). An important stimulus for the development of such smart approaches is the growth of the Internet of Things (IoT) technologies and the increasing investment in the development of green houses, buildings, and, in general, heterogeneous environments. While the benefits for the humans and the environment are evident, a pervasive adoption and distribution of remote monitoring solutions are hindered by the following issue: modeling, designing, prototyping, and further developing the remote applications and underlying architecture require a certain amount of time. Moreover, such systems must be often customized on the basis of the need of the specific domain and involved entities. For such reasons, in this paper, we provide the experience made in addressing some relevant indoor and outdoor case studies through IoT-targeted tools, technologies and protocols, highlighting the advantages and disadvantages of the considered solutions as well as insights that can be useful for future practitioners
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review
Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects
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