Teses / dissertações sobre o tema "The learning space"
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1
Ameur, Foued ben Fredj. "Space-bounded learning algorithms /". Paderborn : Heinz Nixdorf Inst, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007171235&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
2
Kiddle, Rebecca. "Learning outside the box : designing social learning space". Thesis, Oxford Brookes University, 2011. https://radar.brookes.ac.uk/radar/items/f7b36f17-cf4f-4590-8dd7-e6df3ecfc1d2/1/.
Resumo:
Social learning theory asserts that learning involves constructing knowledge through dialogue with others. Traditional learning spaces designed with didactic pedagogies in mind are often not flexible enough to cater to the range of social learning activities promoted by educationalists in classrooms today. This thesis investigates the interaction between social constructivist theories of learning and urban design, developing a body of social learning space design theory as well as space design principles which foster social learning in a university setting. The research uses an 'Enquiry by Design' methodology to develop the principles, basing this enquiry on two case studies: (i) a pilot study analysing an existing social learning space; the Simon Williams Undergraduate Centre, and (ii) an ongoing masterplanning project at Oxford Brookes University's Gipsy Lane campus, Space to Think.
3
Ferreira, Paulo Victor Rodrigues. "SRML: Space Radio Machine Learning". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/199.
Resumo:
Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft's operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector's electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference.
4
Chardonnet, Lucile. "A Shared Learning Space inMidsommarkransen". Thesis, KTH, Arkitektur, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223240.
Resumo:
New school buildings are met with questions in growing cities like Stockholm: How to place them? Which scale? What use, degree of openness, and flexibility, and for whom? This reflection has been made in relation to a suburban area, resulting in a proposal for smaller schools supported by another, shared, building that welcomes more specific activities and is open to the public. It would offer more specialized and adapted spaces to cooking, sewing, music and dance classes, as well as a bigger library and would intensify their use.
5
Kumar, Shailesh. "Modular learning through output space decomposition /". Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004308.
6
Qian, Jing. "Unsupervised learning in high-dimensional space". Thesis, Boston University, 2014. https://hdl.handle.net/2144/12951.
Resumo:
Thesis (Ph.D.)--Boston UniversityIn machine learning, the problem of unsupervised learning is that of trying to explain key features and find hidden structures in unlabeled data. In this thesis we focus on three unsupervised learning scenarios: graph based clustering with imbalanced data, point-wise anomaly detection and anomalous cluster detection on graphs. In the first part we study spectral clustering, a popular graph based clustering technique. We investigate the reason why spectral clustering performs badly on imbalanced and proximal data. We then propose the partition constrained minimum cut (PCut) framework based on a novel parametric graph construction method, that is shown to adapt to different degrees of imbalanced data. We analyze the limit cut behavior of our approach, and demonstrate the significant performance improvement through clustering and semi-supervised learning experiments on imbalanced data. [TRUNCATED]
7
Nichols, B. "Reinforcement learning in continuous state- and action-space". Thesis, University of Westminster, 2014. https://westminsterresearch.westminster.ac.uk/item/967w8/reinforcement-learning-in-continuous-state-and-action-space.
Resumo:
Reinforcement learning in the continuous state-space poses the problem of the inability to store the values of all state-action pairs in a lookup table, due to both storage limitations and the inability to visit all states sufficiently often to learn the correct values. This can be overcome with the use of function approximation techniques with generalisation capability, such as artificial neural networks, to store the value function. When this is applied we can select the optimal action by comparing the values of each possible action; however, when the action-space is continuous this is not possible. In this thesis we investigate methods to select the optimal action when artificial neural networks are used to approximate the value function, through the application of numerical optimization techniques. Although it has been stated in the literature that gradient-ascent methods can be applied to the action selection [47], it is also stated that solving this problem would be infeasible, and therefore, is claimed that it is necessary to utilise a second artificial neural network to approximate the policy function [21, 55]. The major contributions of this thesis include the investigation of the applicability of action selection by numerical optimization methods, including gradient-ascent along with other derivative-based and derivative-free numerical optimization methods,and the proposal of two novel algorithms which are based on the application of two alternative action selection methods: NM-SARSA [40] and NelderMead-SARSA. We empirically compare the proposed methods to state-of-the-art methods from the literature on three continuous state- and action-space control benchmark problems from the literature: minimum-time full swing-up of the Acrobot; Cart-Pole balancing problem; and a double pole variant. We also present novel results from the application of the existing direct policy search method genetic programming to the Acrobot benchmark problem [12, 14].
8
Saeed, Sabina, e Sabina Saeed. "Learning To Learn: A Look Into the Collaborative Learning Space". Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625142.
Resumo:
The Collaborative Learning Space is a recent addition to the University of
Arizona that helps students engage in active learning. Prior to the incorporation of the
CLS, courses were primarily taught in a lecture based format. After receiving a grant, the
University of Arizona decided to use the money to develop new types of leaning spaces
and classrooms to increase student engagement and active learning. These classrooms are
not only effective for the students, but also they
make a huge impact on the faculty. For my honors
senior thesis, I worked with Dr. Cohen to get a
closer look at how we learn and what learning is
like in the CLS. We investigated what types of learning
and teaching styles are used in the CLS, and how
students and professors view this new space in
comparison to a regular lecture based classroom. We
also explored the literature on how active learning
impacts students learning. Overall the teacher and student
satisfaction with the new learning spaces was seen to be very high, and
active learning was found to show improvements in different areas including
information retention, critical thinking, study habits, student attitude, and problem-solving
skills.
9
Mackevicius, Emily Lambert. "Building a state space for song learning". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120871.
Resumo:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 159-177).
Song learning circuitry is thought to operate using a unique representation of each moment within each song syllable. Distinct timestamps for each moment in the song have been observed in the premotor cortical nucleus HVC, where neurons burst in sparse sequences. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Furthermore, young birds learn by imitating a tutor song, and it was previously unclear precisely how the experience of hearing a tutor might shape auditory, motor, and evaluation pathways in the songbird brain. My thesis presents a framework for how these pathways may assemble during early learning, using simple neural mechanisms. I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmically patterned training inputs. I found such a signal when I recorded neurons that project to HVC. When juvenile birds sing, these neurons burst at the beginning of each syllable, and when the birds listen to a tutor, neurons burst at the rhythm of the tutor's song. Bursts marking the beginning of every tutor syllable could seed chains of sequential activity in HVC that could be used to generate the bird's own song imitation. I next used functional calcium imaging to characterize HVC sequences before and after tutor exposure. Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe neural sequences even prior to tutor exposure. Some of these sequences could be tracked as new syllables emerged after tutor exposure, and some sequences appeared to become coupled to the new syllables. In light of my new data, I expand on previous models of song learning to form a detailed hypothesis for how simple neural processes may perform song learning from start to finish.
by Emily Lambert Mackevicius.
Ph. D.
10
Bellocchi, Alberto. "Learning in the third space : a sociocultural perspective on learning with analogies". Queensland University of Technology, 2009. http://eprints.qut.edu.au/30136/.
Resumo:
Research on analogies in science education has focussed on student interpretation of teacher and textbook analogies, psychological aspects of learning with analogies and structured approaches for teaching with analogies. Few studies have investigated how analogies might be pivotal in students’ growing participation in chemical discourse. To study analogies in this way requires a sociocultural perspective on learning that focuses on ways in which language, signs, symbols and practices mediate participation in chemical discourse. This study reports research findings from a teacher-research study of two analogy-writing activities in a chemistry class. The study began with a theoretical model, Third Space, which informed analyses and interpretation of data. Third Space was operationalized into two sub-constructs called Dialogical Interactions and Hybrid Discourses. The aims of this study were to investigate sociocultural aspects of learning chemistry with analogies in order to identify classroom activities where students generate Dialogical Interactions and Hybrid Discourses, and to refine the operationalization of Third Space.
These aims were addressed through three research questions. The research questions were studied through an instrumental case study design. The study was conducted in my Year 11 chemistry class at City State High School for the duration of one Semester. Data were generated through a range of data collection methods and analysed through discourse analysis using the Dialogical Interactions and Hybrid Discourse sub-constructs as coding categories. Results indicated that student interactions differed between analogical activities and mathematical problem-solving activities. Specifically, students drew on discourses other than school chemical discourse to construct analogies and their growing participation in chemical discourse was tracked using the Third Space model as an interpretive lens.
Results of this study led to modification of the theoretical model adopted at the beginning of the study to a new model called Merged Discourse. Merged Discourse represents the mutual relationship that formed during analogical activities between the Analog Discourse and the Target Discourse. This model can be used for interpreting and analysing classroom discourse centred on analogical activities from sociocultural perspectives. That is, it can be used to code classroom discourse to reveal students’ growing participation with chemical (or scientific) discourse consistent with sociocultural perspectives on learning.
11
Sabzpoushan, Maryam. "Play to learn : children learning and activity space". Thesis, KTH, Arkitektur, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-96485.
Resumo:
Play To Learn is an architectural project that aims to create an interwoven series of formal and informal, experimental learning and activity spaces for children between the ages of 3-12 years. Play To Learn creates a new place in an inner city and seaside location where children can come to play, experience, experiment and learn.
12
MAURO, ANA PAULA VIEIRA PEIXOTO. "DESIGN DE E-LEARNING: A SPACE UNDER CONSTRUCTION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13051@1.
Resumo:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROEsta pesquisa discute o papel do designer que atua no desenvolvimento de objetos educacionais virtuais voltados para o treinamento corporativo, inserido em estruturas produtivas de grande porte e com fins lucrativos. Nossa hipótese é a de que o design de e-learning não explora os recursos de hipermídia disponíveis de forma eficiente, em prol do engajamento do aluno. Nosso estudo se estrutura em três partes: primeiramente, dedicamo-nos a entender as estruturas produtivas de empresas com fins lucrativos que desenvolvem o e-learning corporativo e analisamos a atuação do designer nesse contexto. Em um segundo momento, nos debruçamos sobre o potencial oferecido pelo design de hipermídia, a partir das teorias do emotional design e do design de experiência, no sentido de se capturar e de se manter o interesse do usuário aluno. Finalmente, no último capítulo, empreendeu-se um estudo de casos exemplares encontrados na Internet, nos quais identificamos estratégias que visam a promoção de experiências imersivas. A partir dessas características, definimos as categorias que orientaram a análise de um curso a distância, com fins de treinamento corporativo, mediado pela Internet. Concluímos que o designer exerce um importante papel no que tange à usabilidade e ao tratamento informacional dos objetos educacionais virtuais. Entretanto, ao observar-se, por meio da perspectiva oferecida pelas categorias de análise elencadas a aplicação dos recursos utilizados no exemplo do e-learning corporativo, verifica-se que o designer subutiliza o potencial dos recursos de hipermídia hoje disponíveis, os quais poderiam promover um maior grau de envolvimento do aluno com o conteudo educacional apresentado.
This research discusses the role of the designer that works on the development of virtual educational objects regarding the corporate training, inserted in large size for-profit productive structures. Our hypothesis assumes that the e-learning design does not explore, effectively, the available hypermedia resources, for the student`s engagement. Our study id structured in three sections: first, we are dedicated to understanding the productive structures of the for-profit companies that develop the corporate e-learning and we analyze the action of the designer in this context. On a second moment, we debated the potential that hypermedia design offers, from the perspective of the emotional design and the experience design theories, in the sense of capturing and maintaining the user student`s attention. Finally, on the last chapter, a study of cases found on the internet was made, in which we identified strategies that look forward the promotion of immersive experiences. Departing from these features, we defined the categories that guide the analysis of a distance course with the purpose of corporate training through the internet. We conclude that the designer plays an important role in what concerns the usability and the informational treatment of the virtual educational objects. Meanwhile, observing, through the perspective offered by the listed categories of analysis, the application of the used resources in the example of the corporate e-learning, it is verified that the designer underuses the potential of the hypermedia resources available nowadays, which could promote a higher degree of involvement of the student with the educational content presented.
13
Haslam, Bryan (Bryan Todd). "Learning diseases from data : a disease space odyssey". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114002.
Resumo:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 253-280).
Recent commitments to enhance the use of data for learning in medicine provide the opportunity to apply instruments and abstractions from computational learning theory to systematize learning in medicine. The hope is to accelerate the rate at which we incorporate knowledge and improve healthcare quality. In this thesis, we work to bring further clarity to the ways in which computational learning theory can be applied to update the collective knowledge about diseases. Researchers continually study and learn about the complex nature of the human body. They summarize this knowledge with the best possible set of diseases and how those diseases relate to each other. We draw on computational learning theory to understand and broaden this form of collective learning. This mode of collective learning is regarded as unsupervised learning, as no disease labels are initially available. In unsupervised learning, variance is typically reduced to find an optimal function to organize the data. A significant challenge that remains is how to measure variance in the definition of diseases in a comprehensive way. Variance in the definition of a disease introduces a systematic error in both basic and clinical research. If measured, it would also be possible to use computers to efficiently minimize variance, providing a great opportunity for learning by utilizing medical data. In this thesis, we demonstrate that it is possible to estimate variance in the disease taxonomy, effectively estimating an error bar for the current definitions of diseases. We do so using the history of the disease taxonomy and comparing it with a variety of external data sets that relate diseases to attributes such as symptoms, drugs and genes. We demonstrate that variance can be significant over relatively short time periods. We further present methods for updating the disease taxonomy by reducing variance based on external disease data sets. This makes it possible to automatically incorporate information contained in disease data sets into the disease taxonomy. The approach also makes it possible to use expert information encoded in the taxonomy to systematically transfer knowledge and update other biomedical data sets that are often sparse (e.g. - symptoms associated with diseases). A natural question stemming from these results is how granular does data need to be to make improvements? For instance, is patient-level data necessary to enable learning at the macro level of disease? Or are there strategies to extract information from other kinds of data to alleviate the need for very granular data. We show that detailed, patient-level data is not necessarily needed to extract detailed biological data. We do so by comparing disease relationships learned from clinical trial metadata to disease relationships learned from a detailed genetic database and show we can achieve similar results. This result shows that we can use currently available data and take advantage of computational learning to improve disease learning, which suggests a new avenue to improving patient outcomes. By reducing variance within diseases using data available today, we can quickly update the space of diseases to be more precise. Precise diseases lead to better learning in other areas of medicine and ultimately improved healthcare quality.
by Bryan Haslam.
Ph. D.
14
Grönland, Axel, e Möllerstedt Viktor Eriksson. "Robust Reinforcement Learning in Continuous Action/State Space". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293879.
Resumo:
In this project we aim to apply Robust Reinforce-ment Learning algorithms, presented by Doya and Morimoto [1],[2], to control problems. Specifically, we train an agent to balancea pendulum in the unstable equilibrium, which is the invertedstate.We investigate the performance of controllers based on twodifferent function approximators. One is quadratic, and the othermakes use of a Radial Basis Function neural network. To achieverobustness we will make use of an approach similar toH∞control, which amounts to introducing an adversary in the controlsystem.By changing the mass of the pendulum after training, we aimedto show as in [2] that the supposedly robust controllers couldhandle this disruption better than its non-robust counterparts.This was not the case. We also added a random disturber signalafter training and performed similar tests, but we were againunable to show robustness.I detta projekt applicerar vi Robust Rein- forcement Learning (RRL) algoritmer, framtagna av Doya och Morimoto [1], [2], på reglerproblem. Målet var att träna en agent att balansera en pendel i det instabila jämviktsläget; det inverterade tillståndet. Vi undersökte prestandan hos regulatorer baserade på två value function approximators. Den ena är kvadratisk och den andra en Radial Basis Function neuralt nätverk. För att skapa robusthet så använder vi en metod som är ekvivalent med H∞ - reglering, som innebär att man introducerar en motståndare i reglersystemet. Genom att ändra pendelns massa efter träning, hoppas vi att som i [2] kunna visa att den förment robusta regulatorn klarar av denna störning bättre än sin icke-robusta mostvarighet. Detta var inte fallet. Vi lade även till en slumpmässig störsignal efter träning och utförde liknande tester, men lyckades inte visa robusthet i detta fall heller.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
15
Ceylan, Hakan. "Using Reinforcement Learning in Partial Order Plan Space". Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5232/.
Resumo:
Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-visit Monte Carlo method, to partial order planning in order to design agents which do not need any training data or heuristics but are still able to make informed decisions in plan space based on experience. Communicating effectively with the agent is crucial in reinforcement learning. I address how this task was accomplished in plan space and the results from an evaluation of a blocks world test bed.
16
Agiorgitis, Georgios, Mohamed Bennani, Mixalis Drakoularakos e Paul John McConnon. "Digital Wall : The University’s learning and information space". Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-62472.
Resumo:
A North American university is engaged in a large transformation project involving the wholeorganisation. Students, Lecturers, departments and additional questions from the shared leadership team are engaging in defining the services offered, in particular from the university library and its associated sub-services. It is an exciting time for the University as it seeks to not only define its services but also how these can be created and consumed.There are many aspects to this re-organisation and many items to be addressed. In this report,we look at the current situation at the University, as well as taking into account the aspirations of the stakeholders. We start by drawing out a Rich Picture, part of Soft System Methodology (SSM) (Checkland, 1981) which we use in order to spot opportunities that are available for further exploration. Much of the information that we use comes from material supplied by the University itself as well as interviews with the Head of Library services and Head of Library IT. We look indepth at how SSM assists in this process of evaluation through its focus on participation and how it may assist us to understand the many different perspectives collected in our research. SSM consequently assists in defining problems with solutions to any areas that have drawn our attention. Following the evaluation of collected data, discussions and our own observations, we identify that a digital wall that is being proposed for the redesigned library presents an opportunity to explore possibilities for exploitation of this technology. Further research on other digital walls such as Brisbane’s Cube (Abdi et al, 2014), and Auraria Library’s Discovery Wall(Burch, 2016) shows some of the uses that these walls have been put to and how the Institutions use them. We then use a number of models to evaluate the data that we collected on digital walls and from the North American University and analyse it in order to inform our thinking. These models can be used independently or collectively to evaluate data from different perspectives. As such we were able to look at problems and solutions from the perspective of many of the actors involved in shaping the future library services. These models and results are discussed in the report. Finally, we take our results and make a number of proposals for the North American University digital wall along with the relevant justifications at the end of this report.This is a report in the Informatics course 5IK501 during the school year of 2016/2017.
17
Hawasly, Majd. "Policy space abstraction for a lifelong learning agent". Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9931.
Resumo:
This thesis is concerned with policy space abstractions that concisely encode alternative ways of making decisions; dealing with discovery, learning, adaptation and use of these abstractions. This work is motivated by the problem faced by autonomous agents that operate within a domain for long periods of time, hence having to learn to solve many different task instances that share some structural attributes. An example of such a domain is an autonomous robot in a dynamic domestic environment. Such environments raise the need for transfer of knowledge, so as to eliminate the need for long learning trials after deployment. Typically, these tasks would be modelled as sequential decision making problems, including path optimisation for navigation tasks, or Markov Decision Process models for more general tasks. Learning within such models often takes the form of online learning or reinforcement learning. However, handling issues such as knowledge transfer and multiple task instances requires notions of structure and hierarchy, and that raises several questions that form the topic of this thesis – (a) can an agent acquire such hierarchies in policies in an online, incremental manner, (b) can we devise mathematically rigorous ways to abstract policies based on qualitative attributes, (c) when it is inconvenient to employ prolonged trial and error learning, can we devise alternate algorithmic methods for decision making in a lifelong setting? The first contribution of this thesis is an algorithmic method for incrementally acquiring hierarchical policies. Working with the framework of options - temporally extended actions - in reinforcement learning, we present a method for discovering persistent subtasks that define useful options for a particular domain. Our algorithm builds on a probabilistic mixture model in state space to define a generalised and persistent form of ‘bottlenecks’, and suggests suitable policy fragments to make options. In order to continuously update this hierarchy, we devise an incremental process which runs in the background and takes care of proposing and forgetting options. We evaluate this framework in simulated worlds, including the RoboCup 2D simulation league domain. The second contribution of this thesis is in defining abstractions in terms of equivalence classes of trajectories. Utilising recently developed techniques from computational topology, in particular the concept of persistent homology, we show that a library of feasible trajectories could be retracted to representative paths that may be sufficient for reasoning about plans at the abstract level. We present a complete framework, starting from a novel construction of a simplicial complex that describes higher-order connectivity properties of a spatial domain, to methods for computing the homology of this complex at varying resolutions. The resulting abstractions are motion primitives that may be used as topological options, contributing a novel criterion for option discovery. This is validated by experiments in simulated 2D robot navigation, and in manipulation using a physical robot platform. Finally, we develop techniques for solving a family of related, but different, problem instances through policy reuse of a finite policy library acquired over the agent’s lifetime. This represents an alternative approach when traditional methods such as hierarchical reinforcement learning are not computationally feasible. We abstract the policy space using a non-parametric model of performance of policies in multiple task instances, so that decision making is posed as a Bayesian choice regarding what to reuse. This is one approach to transfer learning that is motivated by the needs of practical long-lived systems. We show the merits of such Bayesian policy reuse in simulated real-time interactive systems, including online personalisation and surveillance.
18
Domert, Daniel. "Explorations of University Physics in Abstract Contexts : From de Sitter Space to Learning Space". Doctoral thesis, Uppsala universitet, Fysikundervisningen didaktik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7265.
Resumo:
This is a thesis which contributes to research in two different fields: theoretical physics and physics education research. The common link between these two research areas is that both involve explorations of abstract physics and mathematical representations, but from different perspectives. The first part of this thesis is situated in theoretical physics. Here a cosmological scenario is explored where a de Sitter phase is replaced with a phase described with a scale factor a(t) ~ tq, where 1/3<1. This scenario could be viewed as an inflationary toy model, and is shown to open up the possibility of an information paradox. This potential paradox is resolved even in the worst case scenario by showing that the time scales involved for such a paradox to occur is of the order of magnitude of the recurrence time for the de Sitter space. The second part of this thesis is situated in physics education research. A number of learning situations that are experienced as abstract by students are explored: probability in one dimensional quantum tunnelling; the mindsets that students adopt towards understanding physics equations used in typical teaching scenarios; and what students focus on when presented with physics equations. The results for the quantum scattering study are four phenomenographic categories of description, for the mind sets study, six epistemological components of mindsets and for the focus on physics equations study, three foci creating five levels of increasing complexity of ways of experiencing physics equations. Pedagogical implications of these results are discussed.
19
Filip, Nikitas Metallinos Log, e Lipic Persson Sandra. "Learning in New Space : Knowledge Sourcing for Innovation in Northern Swedish New Space Companies". Thesis, Umeå universitet, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173042.
Resumo:
The New Space industry is a novel branch of the space industry focusing on innovation and commercialization. It experiences very swift growth, although only a fraction of this growth has taken place in Sweden. In order to change this, policymakers are investing funds and efforts into developing the Swedish New Space industry, including the Kvarken Space Center project, aimed at developing the Northern Swedish New Space industry. Here, we see public support in developing a high-tech innovation ecosystem in a peripheral area. This is a topic offering multiple research streams on the most efficient development methods, two of which juxtapose the knowledge ecosystem and intercompany collaborations respectively. With that in mind, we formulated the following research question: How are collaborations and the knowledge ecosystem used to source knowledge for the innovation process? To approach to the subject, we gathered literature on innovation systems and ecosystems in order to analyze the importance of the knowledge ecosystem and the various shapes the industry can assume. This information is linked to theory on knowledge types and sourcing methods considering tacit and codified knowledge, which through different constellations form different needs of knowledge sources. Our empirical approach investigated how the companies used different knowledge sources, namely collaborations, the knowledge ecosystem, and other sources, including networks, monitoring, and mobility. Thereafter, we considered the effects of outstanding factors, including funding and the peripheral region, on knowledge input in innovation. We identified that companies in the upstream industry node, i.e. those related to launch activities and vehicles, and companies in the downstream node, i.e. those extracting data from space, both use engineering knowledge. Engineering knowledge requires both tacit and some codified knowledge, suggesting similar knowledge inputs for both nodes. However, different node traits lead to different inputs. Upstream companies see low degrees of knowledge transfer, especially from the knowledge ecosystem and from collaborations due to NDAs and intellectual property regards, and tacit knowledge input from external sources is particularly lacking. Downstream actors see few constraints to using the investigated knowledge sources, although collaborations saw difficulties due to complexities in structuring them. However, many unilateral complementarities are seen from the knowledge ecosystem, leading to higher knowledge input particularly from networks, while also boosting collaborations to some extent. This was also partly observed in upstream companies. Thus, the knowledge ecosystem sees significant use, although much is indirect, while collaborations see less use. Our main findings are that policymakers and the knowledge ecosystem should focus more on sources of tacit knowledge, such as students, while investing in network-boosting activities as industry events. Companies, especially upstream ones, should utilize collaborations more. Upstream companies should also utilize the local knowledge ecosystem more, as the rights to intellectual property produced by private actors in universities belong to the producer. Regarding future research, we warrant studies on knowledge sourcing in New Space companies and other knowledge sources, such as networks as a compensatory knowledge source.
20
Wenerstrom, Brent K. "Temporal Data Mining in a Dynamic Feature Space". BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/761.
Resumo:
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.
21
Fägerstam, Emilia. "Space and Place : Perspectives on outdoor teaching and learning". Doctoral thesis, Linköpings universitet, Institutionen för beteendevetenskap och lärande, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81318.
Resumo:
This thesis aims to explore teachers’ and students’ experience and perception of outdoor teaching and learning. Further, it aims to explore influences of outdoor teaching on academic performance in biology and mathematics. The contexts for the thesis are a Swedish high school and Australian environmental education centres. The empirical material comprises student and teacher interviews, and questionnaires and tests answered by students. Theoretical frames of reference are theory of place and three dimensions of learning: content, social and emotional dimensions of learning. The results reveal that the extended physical space had the potential to improve social relations and increase participation, collaboration and on-task communication. However, teachers witnessed a period of up to three months before the students adjusted to outdoor teaching. During that time disciplinary issues were a concern. Teaches’ perceptions were that experience of specific places such as local natural environment was fundamental to forming a sense of belonging and environmental concern. However, teachers described children and students as unfamiliar with local natural environments. Teacher’s perceptions were that media provides knowledge about nature rather than direct experience and children and students were often uncomfortable or afraid in nature. Mathematics followed by language education were the subjects most regularly taught outdoors. Two studies compared classroom education with partly outdoor education in biology and mathematics. Results reveal that students’ performance was equally good, or more developed as a consequence of outdoor teaching. An overarching conclusion is that the possibility to appeal to cognitive, social and emotional dimensions of learning all at the same time has the potential to concretize and broaden the often theoretical approach of high school education, and to contribute to long term episodic memories and a desire to learn.Denna avhandling syftar till att utforska lärares och elevers erfarenheter av, och uppfattningar om undervisning och lärande utomhus. Vidare syftar den till att undersöka vilken inverkan undervisning utomhus har på elevers resultat i biologi och matematik. Studierna är utförda i en svensk högstadieskola samt vid australiska miljöutbildningscentra. Det empiriska materialet består av elev- och lärarintervjuer samt enkäter och tester besvarade av elever. Det teoretiska ramverket utgår från platsteori samt ett lärandeperspektiv inkluderande tre dimensioner: innehållsliga, sociala och emotionella dimensioner av lärande. Resultaten visar att utemiljöns utvidgade fysiska rum har potential att förändra sociala relationer positivt och leda till ökat deltagande, samarbete, och kommunikation i ämnet Lärares erfarenhet var dock att det tog upp till tre månader innan eleverna var helt införstådda med utomhusundervisningens innebörd. Under den tiden var oordning i klassen ett hinder. Erfarenhet av specifika platser såsom lokal natur sågs av lärarna som väsentligt för elevernas platstillhörighet och miljöengagemang. Lärare vittnade dock om många elevers främlingskap inför lokala naturmiljöer. Kunskaper om naturen härstammade snarare från media än från egna erfarenheter och eleverna var ofta obekväma eller rädda i naturen. Matematik följt av språk var de ämnen som med störst regelbundenhet undervisades utomhus. I två delstudier jämfördes klassrumsundervisning med undervisning delvis utomhus i biologi och matematik. Resultaten visar på likvärdiga, eller mer utvecklade kunskaper som en följd av utomhusundervisning. En övergripande slutsats är att utomhusundervisningens möjligheter att samtidigt appellera till kognitiva, sociala och emotionella dimensioner av lärande kan konkretisera och vidga högstadieundervisningens teoretiskt inriktade innehåll samt bidra till långlivade episodiska minnen och en lust till lärande.
22
Benveniste, David 1977. "Cognitive conflict in learning three-dimensional space station structures". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/26750.
Resumo:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"September 2004."
Includes bibliographical references (p. 87-88).
(cont.) reached very high values early in the experiment and was significantly but slightly lower in FC than in GC. The target position relative to the subject's body did not affect performance, but subjects responded significantly faster when they were visually upright than when they were upside-down. Although alternative explanations cannot be ruled out, data collected and subjects' comments suggest that unlearning the GC cognitive map posed a significant challenge, and that subjects' knowledge of modules in GC, acquired earlier in the experiment, impeded their learning in FC, at least for the complex FC we used. Results of a Perspective Taking Ability test correlated weakly but significantly with TTR performance in GC, but not in FC. Other tests of spatial skills showed no significant correlation with performance. The effects of motion sickness susceptibility and of gender are also discussed. Supported by NASA Cooperative Agreement NCC 9-58 with the National Space Biomedical Research Institute.
Astronauts train on the ground in normal gravity, in replicas of the space station. Physical constraints force the configuration of these modules on the ground to be different from the configuration of the space station in flight. Based on descriptions of mishaps in human wayfinding (Jonsson 2002), it was hypothesized that the cognitive map of the space station formed from the replicas on the ground could be hard to unlearn. Could the resulting conflict with the actual configuration in flight explain why astronauts lack survey knowledge and often lose track of their orientation? Can they be trained using virtual reality to learn the correct configuration? What makes a configuration hard to learn or unlearn? We studied the ability to learn two realistic and polarized cubic modules in immersive virtual reality. Subjects (n=19) learned these modules first separately, then attached in two different configurations: first a "ground configuration" (GC), then a "flight configuration" (FC). The intrinsic visual verticals of both modules matched in GC, but not in FC, and walls at the interface between the modules were different in the two configurations. Subjects received guided tours of the modules and, through repeated trials, had to predict the location and orientation of one wall (the target), using the wall they were facing. The environment was pseudo-randomly rotated between trials. In the two module environments, subjects were set in the first module and had to place and orient the target wall in the second. The total time to respond to each trial (TTR) and the percent of correct responses (%-correct) were measured. The TTR decreased continuously with time within each virtual environment, but was significantly larger in FC than in GC. %-Correct
by David Benveniste.
S.M.
23
Gaynor, Dónal. "Space and Learning: A case study of their interaction". Thesis, Malmö högskola, Fakulteten för lärande och samhälle (LS), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-32643.
Resumo:
This essay uses a case study to examine in a particular school the nature of these interactions. By examining this case using ethnographic methods including walking tours and interviews the essay gains an insight into how the physical environment interacts with the learning environment in the school. The research identifies four main points of interaction. The need for ownership of space, the quality of the study environment, the atmosphere of the school and the need for privacy. These areas of interaction are identified also within the research with teaching staff at the school. From this research there appears to be evidence in favour of open school models which have significant variation and flexibility of space to allow for both teachers and students to adapt the environment to their various needs. The open school model does however invite significant benefits in terms of non-formal learning situations and new forms of interaction between teachers and students.
24
Peker, Ender. "Campus As An Integrated Learning Environment: Learning In Campus Open Spaces". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612496/index.pdf.
Resumo:
Recent researches on campus learning environments present that there is a seeking for alternative learning spaces among students. Researches argue that more learning is taking place outside of class time than ever before. With an increased emphasis on collaboration and group projects, students are learning in small groups outside of the classrooms as they accomplish work related to their courses. Literature defines these experiences as &lsquoinformal learning&rsquo
. Therewithal, campus open spaces are one of the major areas where students prefer for their informal learning experiences. This thesis aims to search the influence of campus open space design on students&rsquo
learning experiences. Additionally, it argues that there is a strong relation between the learning and the space where learning action occurs. In doing this, it both covers a theoretical framework and a case study. Within the theoretical part, it discusses various learning theories with respect to the prominent principles for each theory. It reveals learning space design indicators which affects learning both in indoor and outdoor learning environments. In the case study, with the analysis of different sample areas from METU campus, the study both investigates the learning experiences actualized on campus open spaces and the triggering design indicators which enhance these experiences.
25
Li, Max Hongming. "Extension on Adaptive MAC Protocol for Space Communications". Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1275.
Resumo:
This work devises a novel approach for mitigating the effects of Catastrophic Forgetting in Deep Reinforcement Learning-based cognitive radio engine implementations employed in space communication applications. Previous implementations of cognitive radio space communication systems utilized a moving window- based online learning method, which discards part of its understanding of the environment each time the window is moved. This act of discarding is called Catastrophic Forgetting. This work investigated ways to control the forgetting process in a more systematic manner, both through a recursive training technique that implements forgetting in a more controlled manner and an ensemble learning technique where each member of the ensemble represents the engine's understanding over a certain period of time. Both of these techniques were integrated into a cognitive radio engine proof-of-concept, and were delivered to the SDR platform on the International Space Station. The results were then compared to the results from the original proof-of-concept. Through comparison, the ensemble learning technique showed promise when comparing performance between training techniques during different communication channel contexts.
26
Brückner, Jörg. "Automatic pattern recognition and learning for information systems". Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262632.
27
Glanville, Ranulph. "Architecture and space for thought". Thesis, Brunel University, 1988. http://bura.brunel.ac.uk/handle/2438/5018.
Resumo:
This thesis is concerned with the description of individual experiences of (architectural) space in a social milieu. Architecture, while considered to be primarily concerned with space as its medium, has a very impoverished (or occasionally, very contorted) verbal language in which to discuss space. The author, as a beginner teacher, noted this in attempts to explore spatial experience with students of architecture, and resolved with their help to generate an appropriate verbal vehicle. The main body of the thesis relates this attempt and accounts for its failure. The Thesis, thus, follows three intertwined streams. 1) A scientific investigation into means for the description of human experience of (architectural) space, using methods developed from Kelly's Personal Construct Theory Repertory Grids. 2) A partially developed spatial analytic language, my personal response to 1) above, which is to be seen as the start of a new research programme that may last many years (the future of which is outlined). 3) An account of a personal learning experience both from, around and through each of these. These streams are organised into three parts. Part 1: Background Studies - into work in associated areas and fields, with an assessment of their relevance to the undertaking presented here. Part 2: The Experiments - attempting (and failing) to create a language, and the transition from verbal to visual, with critical arguments and observations. Part 3: A New Beginning - learning from the failure of Part 2, and the argument for and commencement of a new research programme.
28
Seery, Kristin Kay. "GEOGRAPHIES OF LEARNING IN THE BLACKFEET NATION". UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/290.
Resumo:
Though there is a wealth of theory and research on the relationship between space and identity, few, if any, investigations in geographic literature have examined the relationship between space, identity and education. This research asks the question: In what ways are the spaces of formal education and the spaces of informal education on the Blackfeet reservation similar or different and how does this relationship affect the formation of the identity of the Blackfeet traditional student? For this project, students affiliation with traditional practice is defined by their self-identification and is not connected with their tribal membership status. In interviews, students discuss intersections of education and community and the ways in which the practices and content of learning associated with both spaces affects the learning experience and the self. The research employs a nonessentialist, constitutive phenomenological framework tempered by theories of the productiveness of power, focused on the disidentification of dominant categories through an analysis of: the performativity of agency, the multiple scales of historicity, the situatedness of experience, and the contingent nature of the production of meaning, for the purposes of exploring identity formation, based on the idea that this approach will lead to the elucidation of matters involved in the internalization of the motivation to participate in spaces of learning. The findings show that there is a strong relationship between three elements: spaces of the school that reflect significant aspects of spaces of learning in the community, positive student experiences, and motivation. Also shown, is that the rubric of analysis devised by the researcher, works to break down dominant beliefs regarding the success of traditional Blackfeet students in the school. Finally, a strong case is made for the inclusion of spaces of formal and informal learning in geographic analysis.
29
Hagen, Stephanus Hendrikus Gerhardus ten. "Continuous state space Q-learning for control of nonlinear systems". [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2001. http://dare.uva.nl/document/58530.
30
Qaed, Fatema. "Development of a supportive tool for participatory learning space design". Thesis, Northumbria University, 2015. http://nrl.northumbria.ac.uk/33885/.
Resumo:
All learning occurs within a space, whether this space is physical or virtual, but we have limited knowledge of how learning and teaching relate to it, particularly after a learning space’s users engage and adapt with it. A learning space is seen as a third teacher, but there is limited guidance for teachers on how to adapt designed elements of learning spaces. Therefore, this research aimed to empower teachers’ use of learning space without directly involving designers. It did so by sharing redesign opportunities for learning spaces that facilitate learning and teaching. There were three phases to this research: 1) Contextual review using literature review and observation; 2) Understanding learning space use and potential by investigating classroom space through student drawings, social network data, semi structured interviews, classroom photographs, and teachers’ planning books; and 3) Tool and Exemplar development of a supportive tool formed from structured sets of cards for guidance and inspiration. The first phase revealed a gap between what is written about learning in physical spaces and how these are designed. The second phase studied a range of current teachers’ practices to address this gap, and indicated that although teachers are aware of the importance of physical space, they do not always know how to adapt it to facilitate learning. The results also revealed learning space design elements which designers are unaware of, extending the initial framework from the first phase such. Findings from these studies supported design of a tool (third stage) to empower teachers’ use of space to support different learning and teaching approaches. Evaluation showed that the tool can improve teachers’ awareness of learning space design elements, and enable them to adapt space to support different teaching and learning approaches. Thus research helps both initial learning space designs by architects, as well as subsequent redesign by teachers through development of a practical tool.
31
Richards, Jason T. (Jason Todd) 1975. "Three-dimensional spatial learning in a virtual space station node". Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/69233.
Resumo:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2000.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 77-78).
Astronauts find it difficult to recognize their orientation while facing any of the viewing directions in 6-ported space station node modules. Our previous experiments tested the spatial memory of human subjects in 1-G in an analogous cubic virtual environment and showed that humans are able to learn to orient when instructed to imagine different body orientations while facing in two different directions. Can subjects do the task when facing in all 6 directions? Does training help? Does spatial memory depend on the direction of remembered targets relative to the body? Does performance depend on the subject's ability to rotate himself mentally and use imagery? How long is ability retained after training? 3D spatial learning was studied in two virtual cubic chambers, in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures to a virtual chamber, subjects (n=24) had to memorize the spatial relationships among the 6 pictures around them and learn to predict the direction to a specific picture if they were facing any wall in any roll orientation. After learning in one chamber, the procedure was repeated in a second. Before being tested, subjects received computer-based instructions and practice. Half of subjects were taught to remember logical picture groupings (strategy), while the remaining (control) subjects were free to do the task as they saw fit. Subjects' retention of configurational knowledge (both chambers) and spatial ability (second chamber only, without feedback) were re-tested 1, 7, and 30 days after initial training. Response time (RT) and percent correct (% correct) learning curves were measured on all four days, while configurational knowledge was tested on the last three. All subjects ultimately learned to do the task within 36 trials in either test environment, but performed faster in the second environment than in the first (especially the strategy-trained group). The strategy group showed superior % correct and RT for above/behind targets and generally better configurational knowledge. Retention of configurational knowledge and spatial ability for both groups was good over 30 days. The subjects who reported using mental imagery (n=8) had higher scores on figure rotation tests and % correct for left/right targets. Performances by the control group on the experimental tasks were significantly correlated with those on conventional tests of field independence and 2/3D figure rotation ability. Strategy training helped those who had poorer mental rotation skills, and those who could not use mental imagery. Supported by NASA Cooperative Agreement NCC9-58 with the National Space Biomedical Research Institute, USA.
by Jason T. Richards.
S.M.
32
McHugh, Richard. "Educating 'gangsters' : social space, informal learning and becoming 'gang' involved". Thesis, Sheffield Hallam University, 2017. http://shura.shu.ac.uk/19163/.
Resumo:
This research focuses on the previously neglected topic of how people are educated into groups commonly described as ‘gangs’; in particular, this thesis outlines the role that social space plays in such educative processes. This focus enables both a new contribution to knowledge in the field of ‘gang’ studies and understandings of the way social space is used, understood and perceived by those involved in ‘gangs’. Much research exists in the field of ‘gang’ studies spanning various disciplines and sub-fields. The existing literature on ‘gangs’ predominantly engages with typographies, definitions and prevention; the majority of which stems from a criminological perspective. There has been no direct attempt to explore the ways in which people are educated into ‘gangs’ thus far. Rather than begin from any predetermined assumptions, this research centred on people who have been involved with or affected by ‘gangs’ in order to begin from the lived experiences of those involved or affected. In-depth interviews were carried out with twenty-two participants who are, or were: involved in ‘gangs’; family members of ‘gangs’; and professionals who work with ‘gangs’ (most of whom were previously involved in such groups themselves). Other ethnographic methods were utilised alongside interviews: primarily overt, with some covert participant observations. Ethnographic aspects of the research were undertaken during a twelve-month period in social spaces that were highlighted by participants as being synonymous with, and frequented, by ‘gangs’. This thesis highlights the conditions, structures, agentive responses and social spaces that form the educative processes for becoming involved in ‘gangs’. My contribution to knowledge herein demonstrates how: education within ‘gangs’ takes place through stories, social haunting and reflection within third places and the wider community; occurs under structural conditions but is mediated by agentive choice; social space fosters a community spirit and offers the opportunity to become someone.
33
Köhler, Thomas, Katrin Höhn, Martin Schmauder, Nina Kahnwald e Tanja Schilling. "The SIFA community as a virtual learning space in OSH". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-181537.
Resumo:
In the years 2004 to 2012, a long-term study on the effectiveness of safety experts was commissioned by the German Social Accident Insurance (DGUV). An interdisciplinary team of scientists investigated in these years the activities and the effectiveness of services for professionals responsible for occupational safety and health (OSH). In a case study-like manner it is discussed how the technology that was originally developed as an acquisitions and incentive instrument for the various phases of the safety experts' long-term study, now has become the safety experts’ online community. Both as a stand-alone instrument of prevention as well as a place of learning for professionals for occupational safety, it seems to be a highly appropriate technology. Accompanying the mentoring ensures regular technology updates to meet the increasingly broad use by a growing number of OSH specialists.
34
Choate, James Edwin. "Learning from Frank Lloyd Wright". Thesis, Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/23774.
35
Anagnostopoulou, Kyriaki. "Learning in third space : the nature of non-formal learning opportunities afforded to e-learning leaders in the workplace". Thesis, UCL Institute of Education (IOE), 2014. http://eprints.ioe.ac.uk/18424/.
Resumo:
Institutional initiatives set up to meet the demands of a fast changing higher education (HE) landscape do not comfortably sit within a single academic or administrative department but instead require blended professionals, with a mixed portfolio of work, to operate in third space – between the administrative and academic domains of institutions (Whitchurch, 2008). Heads of e-Learning (HeLs) in UK HE institutions are one such group of professionals who lead the enhancement of learning and teaching through the use of technology. However, one must question how HeLs continue to learn and develop in their roles as transformational leaders to meet the continuous demands posed by the ever-changing HE environment and the evolution of technology. This research explored the affordances of third space as a learning environment, questioned how learning and leadership development take place through non-formal workplace experiences, and sought to relate these back to HeLs’ perceived developmental needs. The concept of liminality (van Gennep, 1960; Turner, 1969) was employed as a theoretical framework, learning was conceptualised as socially constructed identity formation and leadership development was deemed to be a result of learning. A mixed methodological approach was employed and a unique analytical framework shed light on data derived from nine in-depth interviews. Third space environments were found to be ‘expansive’ (Evans et al., 2006), with qualities which afforded transformational learning experiences that permanently altered the ways in which one understands the world around them. Liminal conditions in third space environments provided a means of reconciling a leader’s espoused theories and their theories-in-use, whilst leadership development was linked to learner readiness and the development of credibility. Underpinned by participatory practices, the theory of ‘possible selves’ (Ibarra, 2004) offered a means of understanding transformational learning and development in third space, and brought the concept of leadership closer to active citizenship.
36
Fan, Junchuan. "Modeling space-time activities and places for a smart space —a semantic approach". Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5752.
Resumo:
The rapid advancement of information and communication technologies (ICT) has dramatically changed the way people conduct daily activities. One of the reasons for such advances is the pervasiveness of location-aware devices, and people’s ability to publish and receive information about their surrounding environment. The organization, integration, and analysis of these crowdsensed geographic information is an important task for GIScience research, especially for better understanding place characteristics as well as human activities and movement dynamics in different spaces. In this dissertation research, a semantic modeling and analytic framework based on semantic web technologies is designed to handle information related with human space-time activities (e.g., information about human activities, movement, and surrounding places) for a smart space. Domain ontology for space-time activities and places that captures the essential entities in a spatial domain, and the relationships among them. Based on the developed domain ontology, a Resource Description Framework (RDF) data model is proposed that integrates spatial, temporal and semantic dimensions of space-time activities and places. Three different types of scheduled space-time activities (SXTF, SFTX, SXTX) and their potential spatiotemporal interactions are formalized with OWL and SWRL rules. Using a university campus as an example spatial domain, a RDF knowledgebase is created that integrates scheduled course activities and tweet activities in the campus area. Human movement dynamics for the campus area is analyzed from spatial, temporal, and people’s perspectives using semantic query approach. The ontological knowledge in RDF knowledgebase is further fused with place affordance knowledge learned through training deep learning model on place review data. The integration of place affordance knowledge with people’s intended activities allows the semantic analytic framework to make more personalized location recommendations for people’s daily activities.
37
Gibbings, Peter. "Experience of problem-based learning (PBL) in virtual space : a phenomenographical study". Queensland University of Technology, 2008. http://eprints.qut.edu.au/26423/.
Resumo:
This thesis reports the outcomes of an investigation into students’ experience of Problem-based learning (PBL) in virtual space. PBL is increasingly being used in many fields including engineering education. At the same time many engineering education providers are turning to online distance education. Unfortunately there is a dearth of research into what constitutes an effective learning experience for adult learners who undertake PBL instruction through online distance education. Research was therefore focussed on discovering the qualitatively different ways that students experience PBL in virtual space.
Data was collected in an electronic environment from a course, which adopted the PBL strategy and was delivered entirely in virtual space. Students in this course were asked to respond to open-ended questions designed to elicit their learning experience in the course. Data was analysed using the phenomenographical approach. This interpretative research method concentrated on mapping the qualitative differences in students’ interpretations of their experience in the course. Five qualitatively different ways of experiencing were discovered: Conception 1: ‘A necessary evil for program progression’; Conception 2: ‘Developing skills to understand, evaluate, and solve technical Engineering and Surveying problems’; Conception 3: ‘Developing skills to work effectively in teams in virtual space’; Conception 4: ‘A unique approach to learning how to learn’; Conception 5: ‘Enhancing personal growth’.
Each conception reveals variation in how students attend to learning by PBL in virtual space. Results indicate that the design of students’ online learning experience was responsible for making students aware of deeper ways of experiencing PBL in virtual space. Results also suggest that the quality and quantity of interaction with the team facilitator may have a significant impact on the student experience in virtual PBL courses. The outcomes imply pedagogical strategies can be devised for shifting students’ focus as they engage in the virtual PBL experience to effectively manage the student learning experience and thereby ensure that they gain maximum benefit.
The results from this research hold important ramifications for graduates with respect to their ease of transition into professional work as well as their later professional competence in terms of problem solving, ability to transfer basic knowledge to real-life engineering scenarios, ability to adapt to changes and apply knowledge in unusual situations, ability to think critically and creatively, and a commitment to continuous life-long learning and self-improvement.
38
Wenerstrom, Brent. "Temporal data mining in a dynamic feature space /". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1317.pdf.
39
Zhang, Xinhua, e xinhua zhang cs@gmail com. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding". The Australian National University. ANU College of Engineering and Computer Sciences, 2010. http://thesis.anu.edu.au./public/adt-ANU20100729.072500.
Resumo:
Over the past two decades graphical models have been widely used as powerful tools for compactly representing distributions. On the other hand, kernel methods have been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. Graphical models are a powerful underlying formalism in machine learning. Their graph theoretic properties provide both an intuitive modular interface to model the interacting factors, and a data structure facilitating efficient learning and inference. The probabilistic nature ensures the global consistency of the whole framework, and allows convenient interface of models to data. Kernel methods, on the other hand, provide an effective means of representing rich classes of features for general objects, and at the same time allow efficient search for the optimal model. Recently, kernels have been used to characterize distributions by embedding them into high dimensional feature space. Interestingly, graphical models again decompose this characterization and lead to novel and direct ways of comparing distributions based on samples. Among the many uses of graphical models and kernels, this thesis is devoted to the following four areas: Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modelling the probability of labels given the observations. They have traditionally been trained with using a set of observation and label pairs. Underlying all CRFs is the assumption that, conditioned on the training data, the label sequences of different training examples are independent and identically distributed (iid ). We extended the use of CRFs to a class of temporal learning algorithms, namely policy gradient reinforcement learning (RL). Now the labels are no longer iid. They are actions that update the environment and affect the next observation. From an RL point of view, CRFs provide a natural way to model joint actions in a decentralized Markov decision process. They define how agents can communicate with each other to choose the optimal joint action. We tested our framework on a synthetic network alignment problem, a distributed sensor network, and a road traffic control system. Using tree sampling by Hamze & de Freitas (2004) for inference, the RL methods employing CRFs clearly outperform those which do not model the proper joint policy.
Bayesian online multi-label classification Gaussian density filtering (GDF) provides fast and effective inference for graphical models (Maybeck, 1982). Based on this natural online learner, we propose a Bayesian online multi-label classification (BOMC) framework which learns a probabilistic model of the linear classifier. The training labels are incorporated to update the posterior of the classifiers via a graphical model similar to TrueSkill (Herbrich et al., 2007), and inference is based on GDF with expectation propagation. Using samples from the posterior, we label the test data by maximizing the expected F-score. Our experiments on Reuters1-v2 dataset show that BOMC delivers significantly higher macro-averaged F-score than the state-of-the-art online maximum margin learners such as LaSVM (Bordes et al., 2005) and passive aggressive online learning (Crammer et al., 2006). The online nature of BOMC also allows us to effciently use a large amount of training data.
Hilbert space embedment of distributions
Graphical models are also an essential tool in kernel measures of independence for non-iid data. Traditional information theory often requires density estimation, which makes it unideal for statistical estimation. Motivated by the fact that distributions often appear in machine learning via expectations, we can characterize the distance between distributions in terms of distances between means, especially means in reproducing kernel Hilbert spaces which are called kernel embedment. Under this framework, the undirected graphical models further allow us to factorize the kernel embedment onto cliques, which yields efficient measures of independence for non-iid data (Zhang et al., 2009). We show the effectiveness of this framework for ICA and sequence segmentation, and a number of further applications and research questions are identified. Optimization in maximum margin models for structured data Maximum margin estimation for structured data, e.g. (Taskar et al., 2004), is an important task in machine learning where graphical models also play a key role. They are special cases of regularized risk minimization, for which bundle methods (BMRM, Teo et al., 2007) and the closely related SVMStruct (Tsochantaridis et al., 2005) are state-of-the-art general purpose solvers. Smola et al. (2007b) proved that BMRM requires O(1/έ) iterations to converge to an έ accurate solution, and we further show that this rate hits the lower bound. By utilizing the structure of the objective function, we devised an algorithm for the structured loss which converges to an έ accurate solution in O(1/√έ) iterations. This algorithm originates from Nesterov's optimal first order methods (Nesterov, 2003, 2005b).
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Ryan, Pius. "A case study of a networked learning community : the "third space"". Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/13858.
Resumo:
The purpose of this research was to develop a deeper understanding of the formation, operation, and impacts of a networked learning community within a geographically and culturally diverse school district in British Columbia, Canada. The general approach used for this research was case study methodology. As such, the work must be appreciated as a whole and as a narrative of how something came to be the way it is; in other words, to arrive at a comprehensive understanding of the group under study: Who are its members? What are their stable and recurring modes of activity and interaction? How are they related to one another and how is the group related to the rest of the world? The primary data sources for the study were network participant interviews and documents related to the network. The main findings of the study include a deeper understanding of the impact Ministry and School District level policies and practice had on the network’s inception and evolution; the operational details and structure that supported the network in order to create the conditions for learning; and how the perceived success was based upon focused “teacher talk”. Implications for practice include an understanding of how seemingly simple system actions are influenced by a broad array of macro and micro socio-political actions, as well as the historical context of an organization. The research also suggests that networks are not an end in themselves or fit into a prescribed typology but constitute a shifting terrain with impacts beyond the life of the network.
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Guglielmo, Kennon. "A new learning controller for mechanical manipulators applied in Cartesian space". Thesis, Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/17034.
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Dare, Fadeke Taiye. "A novel methodology for e-learning space design in HEI campuses". Thesis, University of Wolverhampton, 2011. http://hdl.handle.net/2436/209931.
Resumo:
The Higher Education Institution and the Construction Industry are yet to define the most appropriate and effective design parameters for E-learning spaces. Those which exist, focus mainly on cost, budget and timely delivery i.e. the process only not the product. An effective approach to E-learning space design is needed to address the problems of space efficiency, effectiveness, quality, innovativeness, performance and client satisfaction. This study aimed to develop a novel methodology for e-learning space design, by investigating: the impact of e-learning on facilities and design; the impact of e-learning on the design of future spaces; the impact of blended learning on space design; designing for the learn anytime, anywhere paradigm; security issues of e-learning and e-learning space design, the levels of design risk in an e-learning infrastructure and inclusive design issues. A Grounded theory approach was used during initial desk studies, synchronized with a three part forum and pilot survey of 33participants. From this process, two hypotheses emerged; firstly, e-learning space design could affect users‘ learning outcomes and secondly that; user‘s learning requirements were different and varied. To investigate further, site based analyses of 11 HEI‘s, 10 interviews and subsequently a questionnaire survey was administered. Users‘ and stakeholders requirements and good examples of e-learning space design were identified. Data were analysed using a mixed-method research design approach. Three main constructs, Space design, Technology and the E-learning Space Design research focus (ELSD focus), emerged as significant components in the development of a novel framework for the design of e-learning spaces. The relationship between the components is such that the design of spaces with consideration of the ELSD research focus would ensure the effective identification, interpretation and delivery of users‘ requirement while maximising the benefits of the adoption of appropriate technology within HEI facilities. This was therefore proposed as the realistic framework/model for future design of E- learning Spaces in HEI campuses. The framework was adapted into a conceptual design guide to provide guidance for future space design. It is expected the study will support the HEI sector globally as it moves towards achieving best practice solutions to future E-learning space design in HEI campuses.
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Chesters, William Robert. "Robot environment learning with a mixed-linear probabilistic state-space model". Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/6567.
Resumo:
This thesis proposes the use of a probabilistic state-space model with mixed-linear dynamics for learning to predict a robot's experiences. It is motivated by a desire to bridge the gap between traditional models with predefined objective semantics on the one hand, and the biologically-inspired "black box" behavioural paradigm on the other. A novel EM-type algorithm for the model is presented, which is less compuationally demanding than the Monte Carlo techniques developed for use in (for example) visual applications. The algorithm's E-step is slightly approximative, but an extension is described which would in principle make it asymptotically correct. Investigation using synthetically sampled data shows that the uncorrected E-step can any case make correct inferences about quite complicated systems. Results collected from two simulated mobile robot environments support the claim that mixed-linear models can capture both discontinuous and continuous structure in world in an intuitively natural manner; while they proved to perform only slightly better than simpler autoregressive hidden Markov models on these simple tasks, it is possible to claim tentatively that they might scale more effectively to environments in which trends over time played a larger role. Bayesian confidence regions—easily by mixed-linear model— proved be an effective guard for preventing it from making over-confident predictions outside its area of competence. A section on future extensions discusses how the model's easy invertibility could be harnessed to the ultimate aim of choosing actions, from a continuous space of possibilities, which maximise the robot's expected payoff over several steps into the future
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Jacobson, Rupert Daniel. "Exploring geographies of blindness : learning, reading and communicating in geographic space". Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313978.
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Martineau, Maxime. "Deep learning onto graph space : application to image-based insect recognition". Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4024.
Resumo:
Le but de cette thèse est d'étudier la reconnaissance d'insectes comme un problème de reconnaissance des formes basé images. Bien que ce problème ait été étudié en profondeur au long des trois dernières décennies, un aspect reste selon nous toujours à expérimenter à ce jour : les approches profondes (deep learning). À cet effet, la première contribution de cette thèse consiste à déterminer la faisabilité de l'application des réseaux de neurones convolutifs profonds (CNN) au problème de reconnaissance d'images d'insectes. Les limitations majeures ont les suivantes: les images sont très rares et les cardinalités de classes sont hautement déséquilibrées. Pour atténuer ces limitations, le transfer learning et la pondération de la fonction de coûts ont été employés. Des méthodes basées graphes sont également proposées et testées. La première consiste en la conception d'un classificateur de graphes de type perceptron. Le second travail basé sur les graphes de cette thèse est la définition d'un opérateur de convolution pour construire un modèle de réseaux de neurones convolutifs s'appliquant sur les graphes (GCNN.) Le dernier chapitre de la thèse s'applique à utiliser les méthodes mentionnées précédemment à des problèmes de reconnaissance d'images d'insectes. Deux bases d'images sont ici proposées. Là première est constituée d'images prises en laboratoire sur arrière-plan constant. La seconde base est issue de la base ImageNet. Cette base est composée d'images prises en contexte naturel. Les CNN entrainés avec transfer learning sont les plus performants sur ces bases d'imagesThe goal of this thesis is to investigate insect recognition as an image-based pattern recognition problem. Although this problem has been extensively studied along the previous three decades, an element is to the best of our knowledge still to be experimented as of 2017: deep approaches. Therefore, a contribution is about determining to what extent deep convolutional neural networks (CNNs) can be applied to image-based insect recognition. Graph-based representations and methods have also been tested. Two attempts are presented: The former consists in designing a graph-perceptron classifier and the latter graph-based work in this thesis is on defining convolution on graphs to build graph convolutional neural networks. The last chapter of the thesis deals with applying most of the aforementioned methods to insect image recognition problems. Two datasets are proposed. The first one consists of lab-based images with constant background. The second one is generated by taking a ImageNet subset. This set is composed of field-based images. CNNs with transfer learning are the most successful method applied on these datasets
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Kang, Qiwen. "UNSUPERVISED LEARNING IN PHYLOGENOMIC ANALYSIS OVER THE SPACE OF PHYLOGENETIC TREES". UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/39.
Resumo:
A phylogenetic tree is a tree to represent an evolutionary history between species or other entities. Phylogenomics is a new field intersecting phylogenetics and genomics and it is well-known that we need statistical learning methods to handle and analyze a large amount of data which can be generated relatively cheaply with new technologies. Based on the existing Markov models, we introduce a new method, CURatio, to identify outliers in a given gene data set. This method, intrinsically an unsupervised method, can find outliers from thousands or even more genes. This ability to analyze large amounts of genes (even with missing information) makes it unique in many parametric methods. At the same time, the exploration of statistical analysis in high-dimensional space of phylogenetic trees has never stopped, many tree metrics are proposed to statistical methodology. Tropical metric is one of them. We implement a MCMC sampling method to estimate the principal components in a tree space with the tropical metric for achieving dimension reduction and visualizing the result in a 2-D tropical triangle.
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Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks". Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.
Resumo:
Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network's set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector.
Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks.
The research design took a GRN and a machine learning method as input and produced output class < Ατ > and its negation ¬ < Ατ >. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue < 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled.
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Liu, Mingxin. "A COMPARISON OF DEEP LEARNING AND CONVENTIONALALGORITHMS IN NARROW SPACE NAVIGATION". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592492127796282.
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Toombs, Paul. "Reinforcement learning of visually guided spatial goal directed movement". Thesis, University of Stirling, 1997. http://hdl.handle.net/1893/2603.
Resumo:
A range of visually guided, spatial goal directed tasks are investigated, using a computational neuroethology approach. Animats are embedded within a bounded, 2-D environment, and map a 1-D visual array, through a convolution network, to a topography preserving motor array that stochastically determines the direction of movement. Temporal difference reinforcement learning modifies the convolution network in response to a reinforcement signal received only at the goal location. Three forms of visual coding are compared: multiscale coding, where the visual array is convolved by Laplacian of Gaussian filters at a range of spatial scales before convolution to determine the motor array; rectified multiscale coding, where the multiscale array is split into positive and negative components; and intensity coding, where the unfiltered visual array is convolved to determine the motor array. After learning, animats are examined in terms of performance, behaviour and internal structure. When animats learn to approach a solitary circle, of randomly varying contrast, rectified multiscale coding animats learn to outperform multiscale and intensity coding animats in both independent and coarse scale noise conditions. Analysis of the learned internal structure shows that rectified multiscale filtering facilitates learning by enabling detection of the circle at scales least affected by noise. Cartwright and Collett (1983) showed that honeybees learn the angle subtended by a featureless landmark to guide movement to a food source at a fixed distance from the landmark, and furthermore, when tested with only the edges of the landmark, still search in the same location. In a simulation of this experiment, animats are reinforced for moving to where the angle subtended by a solitary circle falls within a certain range. Rectified multiscale filtering leads to better performing animats, with fewer hidden units, in both independent and coarse scale visual noise conditions, though for different reasons in each case. Only those animats with rectified multiscale filtering, that learn in the presence of coarse scale noise, show similar generalisation to the honeybees. Collett, Cartwright and Smith (1986) trained gerbils to search at locations relative to arrangemments of landmarks and tested their search patterns in modifications of the training arrangements. These experiments are simulated with landmark distance coded as either a 1-D intensity array, or a 2-D vector array, plus a simple compass sense. Vector coding animats significantly outperform those using intensity coding and do so with fewer hidden units. Furthermore, vector coding animats show a close match to gerbil behaviour in tests with modified landmark arrangements.
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Haworth, Avril. "The classroom as a heteroglossic space : dialogic talk in small group interaction". Thesis, Lancaster University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302366.