1,046 research outputs found
On Inter-referential Awareness in Collaborative Augmented Reality
For successful collaboration to occur, a workspace must support inter-referential awareness - or the ability for one participant to refer to a set of artifacts in the environment, and for that reference to be correctly interpreted by others. While referring to objects in our everyday environment is a straight-forward task, the non-tangible nature of digital artifacts presents us with new interaction challenges. Augmented reality (AR) is inextricably linked to the physical world, and it is natural to believe that the re-integration of physical artifacts into the workspace makes referencing tasks easier; however, we find that these environments combine the referencing challenges from several computing disciplines, which compound across scenarios. This dissertation presents our studies of this form of awareness in collaborative AR environments. It stems from our research in developing mixed reality environments for molecular modeling, where we explored spatial and multi-modal referencing techniques. To encapsulate the myriad of factors found in collaborative AR, we present a generic, theoretical framework and apply it to analyze this domain. Because referencing is a very human-centric activity, we present the results of an exploratory study which examines the behaviors of participants and how they generate references to physical and virtual content in co-located and remote scenarios; we found that participants refer to content using physical and virtual techniques, and that shared video is highly effective in disambiguating references in remote environments. By implementing user feedback from this study, a follow-up study explores how the environment can passively support referencing, where we discovered the role that virtual referencing plays during collaboration. A third study was conducted in order to better understand the effectiveness of giving and interpreting references using a virtual pointer; the results suggest the need for participants to be parallel with the arrow vector (strengthening the argument for shared viewpoints), as well as the importance of shadows in non-stereoscopic environments. Our contributions include a framework for analyzing the domain of inter-referential awareness, the development of novel referencing techniques, the presentation and analysis of our findings from multiple user studies, and a set of guidelines to help designers support this form of awareness
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
version
Collaborative geographic visualization
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de
Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e
Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative
visualization purposes.
Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment
A Real-Time Augmented Reality System for Industrial Tele-Training
Abstract Augmented Reality (AR) is a departure from standard virtual reality in a sense that it allows users to see computer generated virtual objects superimposed over the real world through the use of see-through head-mounted display. Users of such system can interact in the real/virtual world using additional information, such as 3D virtual models and instructions on how to perform these tasks in the form of video clips, annotations, speech instructions, and images. In this paper, we describe two prototypes of a collaborative industrial Tele-training system. The distributed aspect of this system will enables users on remote sites to collaborate on training tasks by sharing the view of the local user equipped with a wearable computer. The users can interactively manipulate virtual objects that substitute real objects allowing the trainee to try out and discuss the various tasks that needs to be performed. A new technique for identifying real world objects and estimating their coordinates in 3D space is introduced. The method is based on a computer vision technique capable of identifying and locating Binary Square Markers identifying each information stations. Experimental results are presented
Conceitos e métodos para apoio ao desenvolvimento e avaliação de colaboração remota utilizando realidade aumentada
Remote Collaboration using Augmented Reality (AR) shows great
potential to establish a common ground in physically distributed
scenarios where team-members need to achieve a shared goal.
However, most research efforts in this field have been devoted to
experiment with the enabling technology and propose methods to
support its development. As the field evolves, evaluation and
characterization of the collaborative process become an essential,
but difficult endeavor, to better understand the contributions of AR.
In this thesis, we conducted a critical analysis to identify the main
limitations and opportunities of the field, while situating its maturity
and proposing a roadmap of important research actions. Next, a
human-centered design methodology was adopted, involving
industrial partners to probe how AR could support their needs
during remote maintenance. These outcomes were combined with
literature methods into an AR-prototype and its evaluation was
performed through a user study. From this, it became clear the
necessity to perform a deep reflection in order to better understand
the dimensions that influence and must/should be considered in
Collaborative AR. Hence, a conceptual model and a humancentered
taxonomy were proposed to foster systematization of
perspectives. Based on the model proposed, an evaluation
framework for contextualized data gathering and analysis was
developed, allowing support the design and performance of
distributed evaluations in a more informed and complete manner.
To instantiate this vision, the CAPTURE toolkit was created,
providing an additional perspective based on selected dimensions
of collaboration and pre-defined measurements to obtain “in situ”
data about them, which can be analyzed using an integrated
visualization dashboard. The toolkit successfully supported
evaluations of several team-members during tasks of remote
maintenance mediated by AR. Thus, showing its versatility and
potential in eliciting a comprehensive characterization of the added
value of AR in real-life situations, establishing itself as a generalpurpose
solution, potentially applicable to a wider range of
collaborative scenarios.Colaboração Remota utilizando Realidade Aumentada (RA)
apresenta um enorme potencial para estabelecer um entendimento
comum em cenários onde membros de uma equipa fisicamente
distribuídos precisam de atingir um objetivo comum. No entanto, a
maioria dos esforços de investigação tem-se focado nos aspetos
tecnológicos, em fazer experiências e propor métodos para apoiar
seu desenvolvimento. À medida que a área evolui, a avaliação e
caracterização do processo colaborativo tornam-se um esforço
essencial, mas difícil, para compreender as contribuições da RA.
Nesta dissertação, realizámos uma análise crítica para identificar
as principais limitações e oportunidades da área, ao mesmo tempo
em que situámos a sua maturidade e propomos um mapa com
direções de investigação importantes. De seguida, foi adotada uma
metodologia de Design Centrado no Humano, envolvendo
parceiros industriais de forma a compreender como a RA poderia
responder às suas necessidades em manutenção remota. Estes
resultados foram combinados com métodos da literatura num
protótipo de RA e a sua avaliação foi realizada com um caso de
estudo. Ficou então clara a necessidade de realizar uma reflexão
profunda para melhor compreender as dimensões que influenciam
e devem ser consideradas na RA Colaborativa. Foram então
propostos um modelo conceptual e uma taxonomia centrada no ser
humano para promover a sistematização de perspetivas. Com base
no modelo proposto, foi desenvolvido um framework de avaliação
para recolha e análise de dados contextualizados, permitindo
apoiar o desenho e a realização de avaliações distribuídas de
forma mais informada e completa. Para instanciar esta visão, o
CAPTURE toolkit foi criado, fornecendo uma perspetiva adicional
com base em dimensões de colaboração e medidas predefinidas
para obter dados in situ, que podem ser analisados utilizando o
painel de visualização integrado. O toolkit permitiu avaliar com
sucesso vários colaboradores durante a realização de tarefas de
manutenção remota apoiada por RA, permitindo mostrar a sua
versatilidade e potencial em obter uma caracterização abrangente
do valor acrescentado da RA em situações da vida real. Sendo
assim, estabelece-se como uma solução genérica, potencialmente
aplicável a uma gama diversificada de cenários colaborativos.Programa Doutoral em Engenharia Informátic
Cross cultural computer-supported collaboration
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1999.Includes bibliographical references (leaves 120-122).by Grégoire A. Landel.M.Eng
Predicting Creativity in the Wild: Experience Sampling Method and Sociometric Modeling of Movement and Face-To-Face Interactions in Teams
abstract: With the rapid growth of mobile computing and sensor technology, it is now possible to access data from a variety of sources. A big challenge lies in linking sensor based data with social and cognitive variables in humans in real world context. This dissertation explores the relationship between creativity in teamwork, and team members' movement and face-to-face interaction strength in the wild. Using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, three research studies were conducted in academic and industry R&D; labs. Sociometric badges captured movement of team members and face-to-face interaction between team members. KEYS scale was implemented using ESM for self-rated creativity and expert-coded creativity assessment. Activities (movement and face-to-face interaction) and creativity of one five member and two seven member teams were tracked for twenty five days, eleven days, and fifteen days respectively. Day wise values of movement and face-to-face interaction for participants were mean split categorized as creative and non-creative using self- rated creativity measure and expert-coded creativity measure. Paired-samples t-tests [t(36) = 3.132, p < 0.005; t(23) = 6.49 , p < 0.001] confirmed that average daily movement energy during creative days (M = 1.31, SD = 0.04; M = 1.37, SD = 0.07) was significantly greater than the average daily movement of non-creative days (M = 1.29, SD = 0.03; M = 1.24, SD = 0.09). The eta squared statistic (0.21; 0.36) indicated a large effect size. A paired-samples t-test also confirmed that face-to-face interaction tie strength of team members during creative days (M = 2.69, SD = 4.01) is significantly greater [t(41) = 2.36, p < 0.01] than the average face-to-face interaction tie strength of team members for non-creative days (M = 0.9, SD = 2.1). The eta squared statistic (0.11) indicated a large effect size. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data predicted creativity with 87.5% and 91% accuracy respectively. This work advances creativity research and provides a foundation for sensor based real-time creativity support tools for teams.Dissertation/ThesisPh.D. Computer Science 201
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