70,509 research outputs found
Object lessons : a learning object approach to e-learning for social work education
Learning objects are bite-sized digital learning resources designed to tackle the e-learning adoption problem by virtue of their scale, adaptability, and interoperability. The learning object approach advocates the creation of small e-learning resources rather than whole courses: resources that can be mixed and matched; used in a traditional or online learning environment; and adapted for reuse in other discipline areas and in other countries. Storing learning objects within a subject specific digital repository to enable search, discovery, sharing and use adds considerable value to the model. This paper explores the rationale for a learning object approach to e-learning and reflects on early experiences in developing a national learning object repository for social work education in Scotland
The Digital Anatomist Information System and Its Use in the Generation and Delivery of Web-Based Anatomy Atlases
Advances in network and imaging technology, coupled with the availability of 3-D datasets
such as the Visible Human, provide a unique opportunity for developing information systems
in anatomy that can deliver relevant knowledge directly to the clinician, researcher or educator. A software framework is described for developing such a system within a distributed architecture that includes spatial and symbolic anatomy information resources, Web and custom servers, and authoring and end-user client programs. The authoring tools have been used to create 3-D atlases of the brain, knee and thorax that are used both locally and throughout the world. For the one and a half year period from June 1995–January 1997, the on-line atlases were accessed by over 33,000 sites from 94 countries, with an average of over 4000 ‘‘hits’’ per day, and 25,000 hits per day during peak exam periods. The atlases have been linked to by over 500 sites, and have received at least six unsolicited awards by outside rating institutions. The flexibility of the software framework has allowed the information system to evolve with advances in technology and representation methods. Possible new features include knowledge-based image retrieval and tutoring, dynamic generation of 3-D scenes, and eventually, real-time virtual reality navigation through the body. Such features, when coupled with other on-line biomedical information resources, should lead to interesting new ways for
managing and accessing structural information in medicine
Ensuring the discoverability of digital images for social work education : an online tagging survey to test controlled vocabularies
The digital age has transformed access to all kinds of educational content not only in text-based format but also digital images and other media. As learning technologists and librarians begin to organise these new media into digital collections for educational purposes, older problems associated with cataloguing and classifying non-text media have re-emerged. At the heart of this issue is the problem of describing complex and highly subjective images in a reliable and consistent manner. This paper reports on the findings of research designed to test the suitability of two controlled vocabularies to index and thereby improve the discoverability of images stored in the Learning Exchange, a repository for social work education and research. An online survey asked respondents to "tag", a series of images and responses were mapped against the two controlled vocabularies. Findings showed that a large proportion of user generated tags could be mapped to the controlled vocabulary terms (or their equivalents). The implications of these findings for indexing and discovering content are discussed in the context of a wider review of the literature on "folksonomies" (or user tagging) versus taxonomies and controlled vocabularies
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Community Dimmensions of Learning Object Repositories. <i>Deliverable 1</i>: Report on Learning Communities and Repositories
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
Digital Dissemination Platform of Transportation Engineering Education Materials Founded in Adoption Research
INE/AUTC 14.0
Image mining: issues, frameworks and techniques
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper
Mental Health Nurse Prescribing: Challenges in Theory and Practice
This article addresses the historical context of mental health nursing and its relationship to nurse prescribing.; examines some of the theoretical and philosophical forces that have molded modern mental health nursing, discussing the tensions between the medical model and the psychosocial models favoured by many mental health nurse academics and practitioners over the last forty years; and finally discusses the issues and challenges around commencing prescribing in practice, especially when nurse prescribing is not integral to the practitioner’s role.
The article intends to examine the theoretical basis for mental health nurse prescribing, to discuss some of the theoretical tensions which are implicit; and describes briefly the author’s own experience as a recently qualified nurse prescriber
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
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