92,445 research outputs found
Learning style and learning strategies in a multimedia environment
There is a growing realization that it may be expeditious to combine elements from different theories of learning when trying to derive a coherent and usable policy towards computerâmediated learning. Consideration of the subtle distinction between ComputerâAided Learning (CAL) and ComputerâAided Instruction (CAI) conform the basis of a possible classification of computerâmediated learning, and hence of multimedia tools. This classification enables the development of a continuum upon which to place various strategies for computerâmediated learning, and hence a means of broadly classifying multimedia learning tools
Performance of voice and video conferencing over ATM and gigabit ethernet backbone networks
Gigabit Ethernet and ATM network technologies have been modeled as campus network
backbones for the simulation-based comparison of their performance. Real-time voice and
video conferencing traffic is used to compare the performance of both backbone
technologies in terms of response times and packet end-to-end delays. Simulation results
show that Gigabit Ethernet has been able to perform the same and in some cases better than
ATM as a backbone network for video and voice conferencing providing network designers
with a cheaper solution to meet the growing needs of bandwidth-hungry applications in a
campus environment
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
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Applying A Methodology For Educating Students With Special Needs: A Case Study
The introduction of innovative educational technologies opens up new ways of interacting with students. We propose to exploit this potential to help in the education of children with special needs. We analyze the state of the art of tools supporting the teaching process, focusing on the omissions of existing research. We propose a new framework to help throughout the whole teaching process and describe its application to Proyecto Aprender (Learn Project), an educational resource targeting children with learning difficulties. Finally, we outline some conclusions and current/future research lines
Context Aware Adaptable Applications - A global approach
Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to evaluate the consequences of the mobility and corresponding mechanisms to adapt or to be adapted to the new environment. Applications are then qualified as context aware applications. This first part of this paper presents an overview of context and its management by application adaptation. This part starts by a definition and proposes a model for the context. It also presents various techniques to adapt applications to the context: from self-adaptation to supervised approached. The second part is an overview of architectures for adaptable applications. It focuses on platforms based solutions and shows information flows between application, platform and context. Finally it makes a synthesis proposition with a platform for adaptable context-aware applications called Kalimucho. Then we present implementations tools for software components and a dataflow models in order to implement the Kalimucho platform
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