10,167 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
Temporal and numerical expression understanding is of great importance in
many downstream Natural Language Processing (NLP) and Information Retrieval
(IR) tasks. However, much previous work covers only a few sub-types and focuses
only on entity extraction, which severely limits the usability of identified
mentions. In order for such entities to be useful in downstream scenarios,
coverage and granularity of sub-types are important; and, even more so,
providing resolution into concrete values that can be manipulated. Furthermore,
most previous work addresses only a handful of languages. Here we describe a
multi-lingual evaluation dataset - NTX - covering diverse temporal and
numerical expressions across 14 languages and covering extraction,
normalization, and resolution. Along with the dataset we provide a robust
rule-based system as a strong baseline for comparisons against other models to
be evaluated in this dataset. Data and code are available at
\url{https://aka.ms/NTX}.Comment: Technical Repor
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowenâs model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engineâs failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbineâs failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbineâs CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Defining Service Level Agreements in Serverless Computing
The emergence of serverless computing has brought significant advancements to the delivery of computing resources to cloud users. With the abstraction of infrastructure, ecosystem, and execution environments, users could focus on their code while relying on the cloud provider to manage the abstracted layers. In addition, desirable features such as autoscaling and high availability became a providerâs responsibility and can be adopted by the user\u27s application at no extra overhead.
Despite such advancements, significant challenges must be overcome as applications transition from monolithic stand-alone deployments to the ephemeral and stateless microservice model of serverless computing. These challenges pertain to the uniqueness of the conceptual and implementation models of serverless computing. One of the notable challenges is the complexity of defining Service Level Agreements (SLA) for serverless functions. As the serverless model shifts the administration of resources, ecosystem, and execution layers to the provider, users become mere consumers of the providerâs abstracted platform with no insight into its performance. Suboptimal conditions of the abstracted layers are not visible to the end-user who has no means to assess their performance. Thus, SLA in serverless computing must take into consideration the unique abstraction of its model.
This work investigates the Service Level Agreement (SLA) modeling of serverless functions\u27 and serverless chainsâ executions. We highlight how serverless SLA fundamentally differs from earlier cloud delivery models. We then propose an approach to define SLA for serverless functions by utilizing resource utilization fingerprints for functions\u27 executions and a method to assess if executions adhere to that SLA. We evaluate the approachâs accuracy in detecting SLA violations for a broad range of serverless application categories. Our validation results illustrate a high accuracy in detecting SLA violations resulting from resource contentions and providerâs ecosystem degradations. We conclude by presenting the empirical validation of our proposed approach, which could detect Execution-SLA violations with accuracy up to 99%
A Comparative Study on Studentsâ Learning Expectations of Entrepreneurship Education in the UK and China
Entrepreneurship education has become a critical subject in academic research and educational policy design, occupying a central role in contemporary education globally. However, a review of the literature indicates that research on entrepreneurship
education is still in a relatively early stage. Little is known about how entrepreneurship education learning is affected by the environmental context to date. Therefore, combining the institutional context and focusing on studentsâ learning expectations as
a novel perspective, the main aim of the thesis is to address the knowledge gap by developing an original conceptual framework to advance understanding of the dynamic learning process of entrepreneurship education through the lens of self-determination theory, thereby providing a basis for advancing understanding of entrepreneurship education.
The author adopted an epistemological positivism philosophy and a deductive approach. This study gathered 247 valid questionnaires from the UK (84) and China (163). It requested students to recall their learning expectations before attending their entrepreneurship courses and to assess their perceptions of learning outcomes after taking the entrepreneurship courses. It was found that entrepreneurship education policy is an antecedent that influences students' learning expectations, which is
represented in the difference in student autonomy. British students in active learning under a voluntary education policy have higher autonomy than Chinese students in passive learning under a compulsory education policy, thus having higher learning
expectations, leading to higher satisfaction. The positive relationship between autonomy and learning expectations is established, which adds a new dimension to self-determination theory. Furthermore, it is also revealed that the change in studentsâ entrepreneurial intentions before and after their entrepreneurship courses is explained by understanding the process of a business start-up (positive), hands-on business start-up opportunities (positive), studentsâ actual input (positive) and tutorsâ academic qualification (negative).
The thesis makes contributions to both theory and practice. The findings have far reaching implications for different parties, including policymakers, educators, practitioners and researchers. Understanding and shaping students' learning expectations is a critical first step in optimising entrepreneurship education teaching and learning. On the one hand, understanding students' learning expectations of entrepreneurship and entrepreneurship education can help the government with educational interventions and policy reform, as well as improving the quality and delivery of university-based entrepreneurship education. On the other hand, entrepreneurship education can assist students in establishing correct and realistic learning expectations and entrepreneurial conceptions, which will benefit their future entrepreneurial activities and/or employment. An important implication is that this study connects multiple stakeholders by bridging the national-level institutional context, organisational-level university entrepreneurship education, and individual level entrepreneurial learning to promote student autonomy based on an understanding of students' learning expectations. This can help develop graduates with their ability for autonomous learning and autonomous entrepreneurial behaviour.
The results of this study help to remind students that it is them, the learners, their expectations and input that can make the difference between the success or failure of their study. This would not only apply to entrepreneurship education but also to
other fields of study. One key message from this study is that education can be encouraged and supported but cannot be âforcedâ. Mandatory entrepreneurship education is not a quick fix for the lack of university studentsâ innovation and
entrepreneurship. More resources must be invested in enhancing the enterprise culture, thus making entrepreneurship education desirable for students
Educating Sub-Saharan Africa:Assessing Mobile Application Use in a Higher Learning Engineering Programme
In the institution where I teach, insufficient laboratory equipment for engineering education pushed students to learn via mobile phones or devices. Using mobile technologies to learn and practice is not the issue, but the more important question lies in finding out where and how they use mobile tools for learning. Through the lens of Kearney et al.âs (2012) pedagogical model, using authenticity, personalisation, and collaboration as constructs, this case study adopts a mixed-method approach to investigate the mobile learning activities of students and find out their experiences of what works and what does not work. Four questions are borne out of the over-arching research question, âHow do students studying at a University in Nigeria perceive mobile learning in electrical and electronic engineering education?â The first three questions are answered from qualitative, interview data analysed using thematic analysis. The fourth question investigates their collaborations on two mobile social networks using social network and message analysis. The study found how studentsâ mobile learning relates to the real-world practice of engineering and explained ways of adapting and overcoming the mobile toolsâ limitations, and the nature of the collaborations that the students adopted, naturally, when they learn in mobile social networks. It found that mobile engineering learning can be possibly located in an offline mobile zone. It also demonstrates that investigating the effectiveness of mobile learning in the mobile social environment is possible by examining usersâ interactions. The study shows how mobile learning personalisation that leads to impactful engineering learning can be achieved. The study shows how to manage most interface and technical challenges associated with mobile engineering learning and provides a new guide for educators on where and how mobile learning can be harnessed. And it revealed how engineering education can be successfully implemented through mobile tools
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
A productive response to legacy system petrification
Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets
Graphical scaffolding for the learning of data wrangling APIs
In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction
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